require(knitr)
require(png)
require(dplyr)
require(stringr)
require(metafor)
require(compute.es)
require(MuMIn); eval(metafor:::.MuMIn)
require(kableExtra)
require(pander)
require(ggplot2)
require(RColorBrewer)
source("src/acoustic_indices_functions.R")This page describes the systematic review procedure and meta-analytic steps we took while assessing if acoustic indices are reliable indicators of biodiversity.
We extensively searched existing literature for studies assessing the use of acoustic indices as proxies of biodiversity. The systematic search proceeded as follows:
Both peer-reviewed and no peer-reviewed studies were included to avoid publication bias.
After removing all duplicates, we gathered a total of 1,038 studies.
We considered studies eligible for meta-analysis if they met the following inclusion criteria:
Following such inclusion criteria, we screened all selected studies (n = 1,038) based on their abstract, titles and keywords and thereby retaining 142 studies that were identified as potentially eligible. To ascertain their relevance, we conducted a full-text assessment on all these studies, finally retaining 34 studies that passed through all the criteria.
Main extracted data
For each study:
Handling of pseudoreplication
To account for differences in sampling effort among studies and to support the detection of cases of pseudoreplication that might potentially lead to biased statistical tests, we extensively assessed the study design by identifying a total of ten features that summarized both spatial and temporal sampling of each eligible study. When a mismatch between sample size and the number of true replicates was identified, we classified the analysis as using inflated replication and used true replicates as our sample size for meta-analysis.
We used Pearson’s correlation coefficient r, as our measure of effect size. The effect size describes the magnitude of the relationship between acoustic indices and biodiversity. A positive correlation indicates a positive relationship between acoustic indices and biodiversity (i.e. a higher value for the acoustic index corresponds to higher biodiversity) whereas a negative correlation indicates a negative relationship between acoustic indices and biodiversity (i.e. a higher value for the acoustic index corresponds to lower biodiversity).
When Pearson’s correlation, r, could not be directly collected from the studies, we extracted other statistics, such as Spearman’s correlation, t-values, F-values, linear regression slope coefficients and R². If only graphical information was available we extracted the statistics with Web Plot Digitizer v4.2. (Rohatgi, 2019). We converted all statistics to r, using compute.es package in R (Del Re & Del Re, 2012) or when the package did not provide the needed functions we followed the formulas provided in Nakagawa & Cuthill (2007) and Koricheva, Gurevitch, & Mengersen (2013). Whenever study information was insufficient to compute the effect size, we contacted corresponding authors for missing data.
We converted our effect size r to Fisher’s Z in order to satisfy the normality assumption of parametric meta-analysis (Nakagawa & Cuthill, 2007). Fisher’s Z values were converted back to r, to ease interpretation of results.
We collected a total of 481 effect sizes from 35 studies. These number was reduced to 364 effect sizes and 34 studies, after computing composite effect sizes between non-independent effect sizes and removing a study due to difficulty in describing the study design.
df_raw <- read.csv("data/Table.S1.csv")
n_used <- "n_adjusted"
# Use n_adjusted as sample size
df_tidy <- tidy_data(df_raw, n_used)## Removed study id
## 54
## Dataframe aggregated from 481 to 364 entries
# Studies database
studies <- read.csv("data/Table.S2.csv")
df_tidy <- merge(df_tidy, studies, by.x = "id", by.y = "ID", all.x = TRUE)
df_tidy <- df_tidy %>%
mutate(authors = paste(Authors, year)) %>%
select(id, entry, authors, everything(), -Publ_year, -Title,
-doi, -Authors) Table S1 - Complete dataset used in meta-analysis.
kable(df_tidy, format = "html") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
scroll_box(height = "400px", width = "100%")| id | entry | authors | year | impact_factor | index | taxa | environ | bio | diversity_source | pseudoreplication | n | z | var | Journal |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | 15 | Desjonquères et al. 2015 | 2015 | 2.180 | ACI | invertebrates | A | sound_abundance | acoustic | YES | 4 | 0.3285000 | 0.7500000 | PeerJ |
| 2 | 16 | Desjonquères et al. 2015 | 2015 | 2.180 | ACI | invertebrates | A | sound_richness | acoustic | YES | 4 | 0.3322500 | 0.7500000 | PeerJ |
| 2 | 17 | Desjonquères et al. 2015 | 2015 | 2.180 | AR | invertebrates | A | sound_abundance | acoustic | YES | 4 | 0.0692000 | 0.7500000 | PeerJ |
| 2 | 18 | Desjonquères et al. 2015 | 2015 | 2.180 | AR | invertebrates | A | sound_richness | acoustic | YES | 4 | 0.0851500 | 0.7500000 | PeerJ |
| 2 | 19 | Desjonquères et al. 2015 | 2015 | 2.180 | Hf | invertebrates | A | sound_abundance | acoustic | YES | 4 | -0.1851000 | 0.7500000 | PeerJ |
| 2 | 20 | Desjonquères et al. 2015 | 2015 | 2.180 | Hf | invertebrates | A | sound_richness | acoustic | YES | 4 | -0.1304000 | 0.7500000 | PeerJ |
| 2 | 21 | Desjonquères et al. 2015 | 2015 | 2.180 | Ht | invertebrates | A | sound_abundance | acoustic | YES | 4 | -0.3286500 | 0.7500000 | PeerJ |
| 2 | 22 | Desjonquères et al. 2015 | 2015 | 2.180 | Ht | invertebrates | A | sound_richness | acoustic | YES | 4 | -0.2970000 | 0.7500000 | PeerJ |
| 2 | 23 | Desjonquères et al. 2015 | 2015 | 2.180 | M | invertebrates | A | sound_abundance | acoustic | YES | 4 | 0.3453000 | 0.7500000 | PeerJ |
| 2 | 24 | Desjonquères et al. 2015 | 2015 | 2.180 | M | invertebrates | A | sound_richness | acoustic | YES | 4 | 0.3043500 | 0.7500000 | PeerJ |
| 2 | 25 | Desjonquères et al. 2015 | 2015 | 2.180 | NP | invertebrates | A | sound_abundance | acoustic | YES | 4 | 0.2074500 | 0.7500000 | PeerJ |
| 2 | 26 | Desjonquères et al. 2015 | 2015 | 2.180 | NP | invertebrates | A | sound_richness | acoustic | YES | 4 | 0.1851000 | 0.7500000 | PeerJ |
| 4 | 13 | Parks et al. 2014 | 2014 | 1.730 | H | mammals | A | sound_abundance | acoustic | YES | 4 | 0.2801000 | 0.7500000 | Ecol. Inform. |
| 6 | 1 | Boelman et al. 2007 | 2007 | 3.570 | BIO | birds | T | abundance | no_acoustic | NO | 8 | 1.5047000 | 0.2000000 | Ecol. Apli. |
| 9 | 44 | Harris et al. 2016 | 2016 | 5.710 | ACI | fish | A | diversity | no_acoustic | NO | 9 | 1.0278500 | 0.1250250 | Methods Ecol. Evol. |
| 9 | 45 | Harris et al. 2016 | 2016 | 5.710 | ACI | fish | A | richness | no_acoustic | NO | 9 | 0.2877000 | 0.1667000 | Methods Ecol. Evol. |
| 9 | 46 | Harris et al. 2016 | 2016 | 5.710 | AR | fish | A | diversity | no_acoustic | NO | 9 | 0.2104000 | 0.1250250 | Methods Ecol. Evol. |
| 9 | 47 | Harris et al. 2016 | 2016 | 5.710 | AR | fish | A | richness | no_acoustic | NO | 9 | 0.5361000 | 0.1667000 | Methods Ecol. Evol. |
| 9 | 48 | Harris et al. 2016 | 2016 | 5.710 | H | fish | A | diversity | no_acoustic | NO | 9 | 0.3258625 | 0.0937688 | Methods Ecol. Evol. |
| 9 | 49 | Harris et al. 2016 | 2016 | 5.710 | H | fish | A | richness | no_acoustic | NO | 9 | 0.6448500 | 0.1041875 | Methods Ecol. Evol. |
| 10 | 38 | Buscaino et al. 2016 | 2016 | 4.259 | ACI | fish | A | sound_abundance | acoustic | NO | 36 | 0.1003000 | 0.0303000 | Sci Rep |
| 10 | 39 | Buscaino et al. 2016 | 2016 | 4.259 | ACI | invertebrates | A | sound_abundance | acoustic | NO | 36 | 0.0000000 | 0.0303000 | Sci Rep |
| 10 | 170 | Buscaino et al. 2016 | 2016 | 4.259 | ACI | fish | A | sound_abundance | acoustic | NO | 36 | 0.8291000 | 0.0303000 | Sci Rep |
| 10 | 171 | Buscaino et al. 2016 | 2016 | 4.259 | ACI | invertebrates | A | sound_abundance | acoustic | NO | 36 | 0.1717000 | 0.0303000 | Sci Rep |
| 10 | 228 | Buscaino et al. 2016 | 2016 | 4.259 | ACI | fish | A | sound_abundance | acoustic | NO | 36 | 1.2562000 | 0.0303000 | Sci Rep |
| 10 | 229 | Buscaino et al. 2016 | 2016 | 4.259 | ACI | invertebrates | A | sound_abundance | acoustic | NO | 36 | 0.2132000 | 0.0303000 | Sci Rep |
| 10 | 276 | Buscaino et al. 2016 | 2016 | 4.259 | ACI | fish | A | sound_abundance | acoustic | NO | 36 | 0.9076000 | 0.0303000 | Sci Rep |
| 10 | 277 | Buscaino et al. 2016 | 2016 | 4.259 | ACI | invertebrates | A | sound_abundance | acoustic | NO | 36 | 0.1923000 | 0.0303000 | Sci Rep |
| 10 | 300 | Buscaino et al. 2016 | 2016 | 4.259 | ACI | fish | A | sound_abundance | acoustic | NO | 36 | 0.1206000 | 0.0303000 | Sci Rep |
| 10 | 301 | Buscaino et al. 2016 | 2016 | 4.259 | ACI | invertebrates | A | sound_abundance | acoustic | NO | 36 | 0.2554000 | 0.0303000 | Sci Rep |
| 10 | 311 | Buscaino et al. 2016 | 2016 | 4.259 | ACI | fish | A | sound_abundance | acoustic | NO | 36 | 0.0500000 | 0.0303000 | Sci Rep |
| 10 | 312 | Buscaino et al. 2016 | 2016 | 4.259 | ACI | invertebrates | A | sound_abundance | acoustic | NO | 36 | 0.2769000 | 0.0303000 | Sci Rep |
| 10 | 326 | Buscaino et al. 2016 | 2016 | 4.259 | ACI | fish | A | sound_abundance | acoustic | NO | 36 | 0.3428000 | 0.0303000 | Sci Rep |
| 10 | 327 | Buscaino et al. 2016 | 2016 | 4.259 | ACI | invertebrates | A | sound_abundance | acoustic | NO | 36 | 0.8673000 | 0.0303000 | Sci Rep |
| 10 | 339 | Buscaino et al. 2016 | 2016 | 4.259 | ACI | fish | A | sound_abundance | acoustic | NO | 36 | 0.3205000 | 0.0303000 | Sci Rep |
| 10 | 340 | Buscaino et al. 2016 | 2016 | 4.259 | ACI | invertebrates | A | sound_abundance | acoustic | NO | 36 | 1.0454000 | 0.0303000 | Sci Rep |
| 10 | 350 | Buscaino et al. 2016 | 2016 | 4.259 | ACI | fish | A | sound_abundance | acoustic | NO | 36 | 0.3769000 | 0.0303000 | Sci Rep |
| 10 | 351 | Buscaino et al. 2016 | 2016 | 4.259 | ACI | invertebrates | A | sound_abundance | acoustic | NO | 36 | 1.0203000 | 0.0303000 | Sci Rep |
| 10 | 361 | Buscaino et al. 2016 | 2016 | 4.259 | ACI | fish | A | sound_abundance | acoustic | NO | 36 | 0.2237000 | 0.0303000 | Sci Rep |
| 10 | 362 | Buscaino et al. 2016 | 2016 | 4.259 | ACI | invertebrates | A | sound_abundance | acoustic | NO | 36 | 0.9287000 | 0.0303000 | Sci Rep |
| 10 | 363 | Buscaino et al. 2016 | 2016 | 4.259 | ACI | fish | A | sound_abundance | acoustic | NO | 36 | 0.3428000 | 0.0303000 | Sci Rep |
| 10 | 364 | Buscaino et al. 2016 | 2016 | 4.259 | ACI | invertebrates | A | sound_abundance | acoustic | NO | 36 | 0.7582000 | 0.0303000 | Sci Rep |
| 11 | 40 | Bertucci et al. 2016 | 2016 | 4.260 | ACI | fish | A | abundance | no_acoustic | NO | 8 | -0.1262000 | 0.1500000 | Sci Rep |
| 11 | 41 | Bertucci et al. 2016 | 2016 | 4.260 | ACI | fish | A | diversity | no_acoustic | NO | 8 | 1.0328000 | 0.2000000 | Sci Rep |
| 11 | 42 | Bertucci et al. 2016 | 2016 | 4.260 | ACI | fish | A | richness | no_acoustic | NO | 8 | 0.8821000 | 0.1500000 | Sci Rep |
| 11 | 172 | Bertucci et al. 2016 | 2016 | 4.260 | ACI | fish | A | abundance | no_acoustic | NO | 8 | 0.3009000 | 0.1500000 | Sci Rep |
| 11 | 173 | Bertucci et al. 2016 | 2016 | 4.260 | ACI | fish | A | diversity | no_acoustic | NO | 8 | 0.4181000 | 0.2000000 | Sci Rep |
| 11 | 174 | Bertucci et al. 2016 | 2016 | 4.260 | ACI | fish | A | richness | no_acoustic | NO | 8 | 1.1245500 | 0.1500000 | Sci Rep |
| 11 | 230 | Bertucci et al. 2016 | 2016 | 4.260 | ACI | fish | A | abundance | no_acoustic | NO | 8 | -0.1797000 | 0.1500000 | Sci Rep |
| 11 | 231 | Bertucci et al. 2016 | 2016 | 4.260 | ACI | fish | A | diversity | no_acoustic | NO | 8 | 0.8429000 | 0.2000000 | Sci Rep |
| 11 | 232 | Bertucci et al. 2016 | 2016 | 4.260 | ACI | fish | A | richness | no_acoustic | NO | 8 | 0.6372500 | 0.1500000 | Sci Rep |
| 11 | 278 | Bertucci et al. 2016 | 2016 | 4.260 | ACI | fish | A | abundance | no_acoustic | NO | 8 | -0.0471500 | 0.1500000 | Sci Rep |
| 11 | 279 | Bertucci et al. 2016 | 2016 | 4.260 | ACI | fish | A | diversity | no_acoustic | NO | 8 | 0.6747000 | 0.2000000 | Sci Rep |
| 11 | 280 | Bertucci et al. 2016 | 2016 | 4.260 | ACI | fish | A | richness | no_acoustic | NO | 8 | 0.6980000 | 0.1500000 | Sci Rep |
| 13 | 11 | McWilliam & Hawkin 2013 | 2013 | 2.480 | ACI | invertebrates | A | sound_abundance | acoustic | YES | 5 | 1.1881000 | 0.5000000 | J. ExpMar. Biol. Ecol |
| 13 | 12 | McWilliam & Hawkin 2013 | 2013 | 2.480 | ADI | invertebrates | A | sound_abundance | acoustic | YES | 5 | 1.3331000 | 0.5000000 | J. ExpMar. Biol. Ecol |
| 14 | 34 | Wa Maina et al. 2016 | 2016 | 1.220 | ACI | birds | T | richness | acoustic | NO | 8 | 0.1689000 | 0.2000000 | BDJ |
| 14 | 35 | Wa Maina et al. 2016 | 2016 | 1.220 | ACI | birds | T | richness | no_acoustic | NO | 8 | 0.9638000 | 0.2000000 | BDJ |
| 14 | 36 | Wa Maina et al. 2016 | 2016 | 1.220 | H | birds | T | richness | acoustic | NO | 8 | 0.4676000 | 0.2000000 | BDJ |
| 14 | 37 | Wa Maina et al. 2016 | 2016 | 1.220 | H | birds | T | richness | no_acoustic | NO | 8 | 0.5870000 | 0.2000000 | BDJ |
| 15 | 43 | Roca & Proulx 2016 | 2016 | 4.810 | H | invertebrates | T | richness | acoustic | NO | 4 | 1.8972000 | 1.0000000 | Ecology |
| 15 | 175 | Roca & Proulx 2016 | 2016 | 4.810 | H | invertebrates | T | richness | acoustic | NO | 4 | 2.6467000 | 1.0000000 | Ecology |
| 15 | 233 | Roca & Proulx 2016 | 2016 | 4.810 | H | invertebrates | T | richness | acoustic | NO | 4 | 2.3796000 | 1.0000000 | Ecology |
| 17 | 14 | Zhang et al. 2015 | 2015 | 0.000 | ACI | birds | T | richness | acoustic | YES | 4 | 0.3940000 | 1.0000000 | IEEE.Conference.procedings |
| 37 | 33 | Picciulin et al. 2016 | 2016 | 0.000 | ACI | fish | A | sound_abundance | acoustic | YES | 4 | 0.3260000 | 1.0000000 | Proceedings of Meetings on Acoustics |
| 41 | 3 | Paisley-Jones 2011 | 2011 | 0.000 | H | birds | T | sound_abundance | acoustic | NO | 6 | 0.1481000 | 0.3333000 | thesis |
| 41 | 4 | Paisley-Jones 2011 | 2011 | 0.000 | H | invertebrates | T | diversity | no_acoustic | NO | 6 | -0.1430000 | 0.3333000 | thesis |
| 44 | 86 | Machado et al. 2017 | 2017 | 4.994 | ADI | birds | T | richness | acoustic | NO | 30 | 0.4910000 | 0.0370000 | Landsc. Urban Plan. |
| 44 | 87 | Machado et al. 2017 | 2017 | 4.994 | NDSI | birds | T | richness | acoustic | NO | 30 | 0.1686000 | 0.0370000 | Landsc. Urban Plan. |
| 45 | 7 | McLaren 2012 | 2012 | 0.000 | NDSI | birds | T | diversity | no_acoustic | NO | 36 | 0.9330000 | 0.0303000 | practicum |
| 45 | 8 | McLaren 2012 | 2012 | 0.000 | NDSI | birds | T | richness | acoustic | NO | 36 | 0.6070000 | 0.0303000 | practicum |
| 45 | 9 | McLaren 2012 | 2012 | 0.000 | NDSI | birds | T | richness | no_acoustic | NO | 36 | 0.9160000 | 0.0303000 | practicum |
| 53 | 154 | Moreno-Gomez 2019 | 2019 | 4.490 | ACI | anurans | T | richness | acoustic | NO | 33 | 0.0000000 | 0.0333000 | Ecol. Indic. |
| 53 | 155 | Moreno-Gomez 2019 | 2019 | 4.490 | ACI | birds | T | richness | acoustic | NO | 33 | -0.0209000 | 0.0333000 | Ecol. Indic. |
| 53 | 156 | Moreno-Gomez 2019 | 2019 | 4.490 | ADI | anurans | T | richness | acoustic | NO | 33 | 0.1051000 | 0.0333000 | Ecol. Indic. |
| 53 | 157 | Moreno-Gomez 2019 | 2019 | 4.490 | ADI | birds | T | richness | acoustic | NO | 33 | -0.3237000 | 0.0333000 | Ecol. Indic. |
| 53 | 158 | Moreno-Gomez 2019 | 2019 | 4.490 | AEI | anurans | T | richness | acoustic | NO | 33 | -0.2122000 | 0.0333000 | Ecol. Indic. |
| 53 | 159 | Moreno-Gomez 2019 | 2019 | 4.490 | AEI | birds | T | richness | acoustic | NO | 33 | 0.3237000 | 0.0333000 | Ecol. Indic. |
| 53 | 160 | Moreno-Gomez 2019 | 2019 | 4.490 | BIO | anurans | T | richness | acoustic | NO | 33 | 0.1051000 | 0.0333000 | Ecol. Indic. |
| 53 | 161 | Moreno-Gomez 2019 | 2019 | 4.490 | BIO | birds | T | richness | acoustic | NO | 33 | -0.1051000 | 0.0333000 | Ecol. Indic. |
| 53 | 162 | Moreno-Gomez 2019 | 2019 | 4.490 | H | anurans | T | richness | acoustic | NO | 33 | 0.1051000 | 0.0333000 | Ecol. Indic. |
| 53 | 163 | Moreno-Gomez 2019 | 2019 | 4.490 | H | birds | T | richness | acoustic | NO | 33 | -0.1051000 | 0.0333000 | Ecol. Indic. |
| 53 | 164 | Moreno-Gomez 2019 | 2019 | 4.490 | Hf | anurans | T | richness | acoustic | NO | 33 | -0.1051000 | 0.0333000 | Ecol. Indic. |
| 53 | 165 | Moreno-Gomez 2019 | 2019 | 4.490 | Hf | birds | T | richness | acoustic | NO | 33 | 0.1051000 | 0.0333000 | Ecol. Indic. |
| 53 | 166 | Moreno-Gomez 2019 | 2019 | 4.490 | Ht | anurans | T | richness | acoustic | NO | 33 | 0.1051000 | 0.0333000 | Ecol. Indic. |
| 53 | 167 | Moreno-Gomez 2019 | 2019 | 4.490 | Ht | birds | T | richness | acoustic | NO | 33 | -0.3237000 | 0.0333000 | Ecol. Indic. |
| 53 | 214 | Moreno-Gomez 2019 | 2019 | 4.490 | ACI | anurans | T | richness | acoustic | NO | 11 | -0.1051000 | 0.1250000 | Ecol. Indic. |
| 53 | 215 | Moreno-Gomez 2019 | 2019 | 4.490 | ACI | birds | T | richness | acoustic | NO | 11 | 0.3237000 | 0.1250000 | Ecol. Indic. |
| 53 | 216 | Moreno-Gomez 2019 | 2019 | 4.490 | ADI | anurans | T | richness | acoustic | NO | 11 | 0.1051000 | 0.1250000 | Ecol. Indic. |
| 53 | 217 | Moreno-Gomez 2019 | 2019 | 4.490 | ADI | birds | T | richness | acoustic | NO | 11 | -0.2122000 | 0.1250000 | Ecol. Indic. |
| 53 | 218 | Moreno-Gomez 2019 | 2019 | 4.490 | AEI | anurans | T | richness | acoustic | NO | 11 | -0.1051000 | 0.1250000 | Ecol. Indic. |
| 53 | 219 | Moreno-Gomez 2019 | 2019 | 4.490 | AEI | birds | T | richness | acoustic | NO | 11 | 0.3237000 | 0.1250000 | Ecol. Indic. |
| 53 | 220 | Moreno-Gomez 2019 | 2019 | 4.490 | BIO | anurans | T | richness | acoustic | NO | 11 | -0.1051000 | 0.1250000 | Ecol. Indic. |
| 53 | 221 | Moreno-Gomez 2019 | 2019 | 4.490 | BIO | birds | T | richness | acoustic | NO | 11 | 0.2122000 | 0.1250000 | Ecol. Indic. |
| 53 | 222 | Moreno-Gomez 2019 | 2019 | 4.490 | H | anurans | T | richness | acoustic | NO | 11 | 0.1051000 | 0.1250000 | Ecol. Indic. |
| 53 | 223 | Moreno-Gomez 2019 | 2019 | 4.490 | H | birds | T | richness | acoustic | NO | 11 | -0.3237000 | 0.1250000 | Ecol. Indic. |
| 53 | 224 | Moreno-Gomez 2019 | 2019 | 4.490 | Hf | anurans | T | richness | acoustic | NO | 11 | -0.0105000 | 0.1250000 | Ecol. Indic. |
| 53 | 225 | Moreno-Gomez 2019 | 2019 | 4.490 | Hf | birds | T | richness | acoustic | NO | 11 | -0.1051000 | 0.1250000 | Ecol. Indic. |
| 53 | 226 | Moreno-Gomez 2019 | 2019 | 4.490 | Ht | anurans | T | richness | acoustic | NO | 11 | 0.1051000 | 0.1250000 | Ecol. Indic. |
| 53 | 227 | Moreno-Gomez 2019 | 2019 | 4.490 | Ht | birds | T | richness | acoustic | NO | 11 | -0.4426000 | 0.1250000 | Ecol. Indic. |
| 53 | 262 | Moreno-Gomez 2019 | 2019 | 4.490 | ACI | anurans | T | richness | acoustic | NO | 32 | 0.1051000 | 0.0345000 | Ecol. Indic. |
| 53 | 263 | Moreno-Gomez 2019 | 2019 | 4.490 | ACI | birds | T | richness | acoustic | NO | 32 | 0.5731000 | 0.0345000 | Ecol. Indic. |
| 53 | 264 | Moreno-Gomez 2019 | 2019 | 4.490 | ADI | anurans | T | richness | acoustic | NO | 32 | 0.1051000 | 0.0345000 | Ecol. Indic. |
| 53 | 265 | Moreno-Gomez 2019 | 2019 | 4.490 | ADI | birds | T | richness | acoustic | NO | 32 | -0.1051000 | 0.0345000 | Ecol. Indic. |
| 53 | 266 | Moreno-Gomez 2019 | 2019 | 4.490 | AEI | anurans | T | richness | acoustic | NO | 32 | -0.1051000 | 0.0345000 | Ecol. Indic. |
| 53 | 267 | Moreno-Gomez 2019 | 2019 | 4.490 | AEI | birds | T | richness | acoustic | NO | 32 | 0.2122000 | 0.0345000 | Ecol. Indic. |
| 53 | 268 | Moreno-Gomez 2019 | 2019 | 4.490 | BIO | anurans | T | richness | acoustic | NO | 32 | -0.2122000 | 0.0345000 | Ecol. Indic. |
| 53 | 269 | Moreno-Gomez 2019 | 2019 | 4.490 | BIO | birds | T | richness | acoustic | NO | 32 | 0.3237000 | 0.0345000 | Ecol. Indic. |
| 53 | 270 | Moreno-Gomez 2019 | 2019 | 4.490 | H | anurans | T | richness | acoustic | NO | 32 | 0.0105000 | 0.0345000 | Ecol. Indic. |
| 53 | 271 | Moreno-Gomez 2019 | 2019 | 4.490 | H | birds | T | richness | acoustic | NO | 32 | 0.3237000 | 0.0345000 | Ecol. Indic. |
| 53 | 272 | Moreno-Gomez 2019 | 2019 | 4.490 | Hf | anurans | T | richness | acoustic | NO | 32 | 0.0000000 | 0.0345000 | Ecol. Indic. |
| 53 | 273 | Moreno-Gomez 2019 | 2019 | 4.490 | Hf | birds | T | richness | acoustic | NO | 32 | 0.3237000 | 0.0345000 | Ecol. Indic. |
| 53 | 274 | Moreno-Gomez 2019 | 2019 | 4.490 | Ht | anurans | T | richness | acoustic | NO | 32 | 0.0105000 | 0.0345000 | Ecol. Indic. |
| 53 | 275 | Moreno-Gomez 2019 | 2019 | 4.490 | Ht | birds | T | richness | acoustic | NO | 32 | -0.4426000 | 0.0345000 | Ecol. Indic. |
| 60 | 152 | Patrick Lyon et al. 2019 | 2019 | 2.360 | ACI | fish | A | abundance | no_acoustic | NO | 7 | -0.3654000 | 0.2500000 | Mar. Ecol.-Prog. Ser. |
| 60 | 153 | Patrick Lyon et al. 2019 | 2019 | 2.360 | ACI | fish | A | diversity | no_acoustic | NO | 7 | 0.1306000 | 0.1875000 | Mar. Ecol.-Prog. Ser. |
| 60 | 212 | Patrick Lyon et al. 2019 | 2019 | 2.360 | ACI | fish | A | abundance | no_acoustic | NO | 7 | 0.5763000 | 0.2500000 | Mar. Ecol.-Prog. Ser. |
| 60 | 213 | Patrick Lyon et al. 2019 | 2019 | 2.360 | ACI | fish | A | diversity | no_acoustic | NO | 7 | 0.4313500 | 0.1875000 | Mar. Ecol.-Prog. Ser. |
| 70 | 94 | Bolgan et al. 2018 | 2018 | 4.010 | ACI | fish | A | sound_abundance | acoustic | NO | 9 | -0.2232000 | 0.1250250 | Sci Rep |
| 70 | 95 | Bolgan et al. 2018 | 2018 | 4.010 | ACI | fish | A | sound_richness | acoustic | NO | 9 | 0.6011000 | 0.1667000 | Sci Rep |
| 70 | 199 | Bolgan et al. 2018 | 2018 | 4.010 | ACI | fish | A | sound_abundance | acoustic | NO | 4 | 0.0262000 | 1.0000000 | Sci Rep |
| 70 | 200 | Bolgan et al. 2018 | 2018 | 4.010 | ACI | fish | A | sound_abundance | acoustic | NO | 9 | 0.6011000 | 0.1667000 | Sci Rep |
| 70 | 201 | Bolgan et al. 2018 | 2018 | 4.010 | ACI | fish | A | sound_abundance | acoustic | NO | 10 | 0.4676000 | 0.1429000 | Sci Rep |
| 70 | 202 | Bolgan et al. 2018 | 2018 | 4.010 | ACI | fish | A | sound_richness | acoustic | NO | 9 | 0.1051000 | 0.1667000 | Sci Rep |
| 70 | 203 | Bolgan et al. 2018 | 2018 | 4.010 | ACI | fish | A | sound_richness | acoustic | NO | 10 | 0.4931000 | 0.1429000 | Sci Rep |
| 70 | 256 | Bolgan et al. 2018 | 2018 | 4.010 | ACI | fish | A | sound_abundance | acoustic | NO | 9 | 0.8244000 | 0.1667000 | Sci Rep |
| 70 | 257 | Bolgan et al. 2018 | 2018 | 4.010 | ACI | fish | A | sound_richness | acoustic | NO | 9 | 1.2973000 | 0.1667000 | Sci Rep |
| 70 | 294 | Bolgan et al. 2018 | 2018 | 4.010 | ACI | fish | A | sound_abundance | acoustic | YES | 25 | 0.3422714 | 0.0260000 | Sci Rep |
| 70 | 295 | Bolgan et al. 2018 | 2018 | 4.010 | ACI | fish | A | sound_richness | acoustic | YES | 25 | 0.2930143 | 0.0260000 | Sci Rep |
| 77 | 71 | Fairbrass et al. 2017 | 2017 | 3.980 | ACI | several | T | richness | acoustic | NO | 105 | 0.8513000 | 0.0099000 | Ecol. Indic. |
| 77 | 80 | Fairbrass et al. 2017 | 2017 | 3.980 | BIO | several | T | richness | acoustic | NO | 105 | 0.4545000 | 0.0099000 | Ecol. Indic. |
| 77 | 85 | Fairbrass et al. 2017 | 2017 | 3.980 | NDSI | several | T | richness | acoustic | NO | 105 | 0.4320000 | 0.0099000 | Ecol. Indic. |
| 77 | 186 | Fairbrass et al. 2017 | 2017 | 3.980 | ADI | several | T | richness | acoustic | NO | 105 | 0.1968000 | 0.0099000 | Ecol. Indic. |
| 80 | 62 | Staaterman et al. 2017 | 2017 | 2.280 | ACI | fish | A | abundance | no_acoustic | NO | 4 | -0.1203000 | 0.6666667 | Mar. Ecol.-Prog. Ser. |
| 80 | 63 | Staaterman et al. 2017 | 2017 | 2.280 | ACI | fish | A | diversity | no_acoustic | NO | 4 | 0.0000000 | 0.7500000 | Mar. Ecol.-Prog. Ser. |
| 80 | 64 | Staaterman et al. 2017 | 2017 | 2.280 | ACI | fish | A | richness | no_acoustic | NO | 4 | -0.2158000 | 0.6666667 | Mar. Ecol.-Prog. Ser. |
| 80 | 65 | Staaterman et al. 2017 | 2017 | 2.280 | H | fish | A | abundance | no_acoustic | NO | 4 | 0.4060667 | 0.6666667 | Mar. Ecol.-Prog. Ser. |
| 80 | 66 | Staaterman et al. 2017 | 2017 | 2.280 | H | fish | A | diversity | no_acoustic | NO | 4 | -0.1586500 | 0.7500000 | Mar. Ecol.-Prog. Ser. |
| 80 | 67 | Staaterman et al. 2017 | 2017 | 2.280 | H | fish | A | richness | no_acoustic | NO | 4 | 0.3178667 | 0.6666667 | Mar. Ecol.-Prog. Ser. |
| 80 | 176 | Staaterman et al. 2017 | 2017 | 2.280 | ACI | fish | A | abundance | no_acoustic | NO | 4 | 0.0335667 | 0.6666667 | Mar. Ecol.-Prog. Ser. |
| 80 | 177 | Staaterman et al. 2017 | 2017 | 2.280 | ACI | fish | A | diversity | no_acoustic | NO | 4 | 0.1990500 | 0.7500000 | Mar. Ecol.-Prog. Ser. |
| 80 | 178 | Staaterman et al. 2017 | 2017 | 2.280 | ACI | fish | A | richness | no_acoustic | NO | 4 | 0.0357000 | 0.6666667 | Mar. Ecol.-Prog. Ser. |
| 80 | 179 | Staaterman et al. 2017 | 2017 | 2.280 | H | fish | A | abundance | no_acoustic | NO | 4 | 0.4564000 | 0.6666667 | Mar. Ecol.-Prog. Ser. |
| 80 | 180 | Staaterman et al. 2017 | 2017 | 2.280 | H | fish | A | diversity | no_acoustic | NO | 4 | -0.2144000 | 0.7500000 | Mar. Ecol.-Prog. Ser. |
| 80 | 181 | Staaterman et al. 2017 | 2017 | 2.280 | H | fish | A | richness | no_acoustic | NO | 4 | 0.4316333 | 0.6666667 | Mar. Ecol.-Prog. Ser. |
| 80 | 234 | Staaterman et al. 2017 | 2017 | 2.280 | ACI | fish | A | abundance | no_acoustic | NO | 4 | 0.0357000 | 0.6666667 | Mar. Ecol.-Prog. Ser. |
| 80 | 235 | Staaterman et al. 2017 | 2017 | 2.280 | ACI | fish | A | diversity | no_acoustic | NO | 4 | -0.2144000 | 0.7500000 | Mar. Ecol.-Prog. Ser. |
| 80 | 236 | Staaterman et al. 2017 | 2017 | 2.280 | ACI | fish | A | richness | no_acoustic | NO | 4 | -0.3736000 | 0.6666667 | Mar. Ecol.-Prog. Ser. |
| 80 | 237 | Staaterman et al. 2017 | 2017 | 2.280 | H | fish | A | abundance | no_acoustic | NO | 4 | 0.2231333 | 0.6666667 | Mar. Ecol.-Prog. Ser. |
| 80 | 238 | Staaterman et al. 2017 | 2017 | 2.280 | H | fish | A | diversity | no_acoustic | NO | 4 | 0.1093000 | 0.7500000 | Mar. Ecol.-Prog. Ser. |
| 80 | 239 | Staaterman et al. 2017 | 2017 | 2.280 | H | fish | A | richness | no_acoustic | NO | 4 | 0.0417667 | 0.6666667 | Mar. Ecol.-Prog. Ser. |
| 80 | 281 | Staaterman et al. 2017 | 2017 | 2.280 | ACI | fish | A | abundance | no_acoustic | NO | 4 | 0.0357000 | 0.6666667 | Mar. Ecol.-Prog. Ser. |
| 80 | 282 | Staaterman et al. 2017 | 2017 | 2.280 | ACI | fish | A | diversity | no_acoustic | NO | 4 | -0.1586500 | 0.7500000 | Mar. Ecol.-Prog. Ser. |
| 80 | 283 | Staaterman et al. 2017 | 2017 | 2.280 | ACI | fish | A | richness | no_acoustic | NO | 4 | -0.3385667 | 0.6666667 | Mar. Ecol.-Prog. Ser. |
| 80 | 284 | Staaterman et al. 2017 | 2017 | 2.280 | H | fish | A | abundance | no_acoustic | NO | 4 | 0.2231333 | 0.6666667 | Mar. Ecol.-Prog. Ser. |
| 80 | 285 | Staaterman et al. 2017 | 2017 | 2.280 | H | fish | A | diversity | no_acoustic | NO | 4 | -0.1061000 | 0.7500000 | Mar. Ecol.-Prog. Ser. |
| 80 | 286 | Staaterman et al. 2017 | 2017 | 2.280 | H | fish | A | richness | no_acoustic | NO | 4 | 0.2055667 | 0.6666667 | Mar. Ecol.-Prog. Ser. |
| 86 | 92 | Indraswari et al. 2018 | 2018 | 3.400 | ACI | anurans | T | sound_abundance | acoustic | YES | 33 | 0.6284000 | 0.0333000 | Freshw. Biol. |
| 86 | 93 | Indraswari et al. 2018 | 2018 | 3.400 | Ht | anurans | T | sound_abundance | acoustic | YES | 33 | 0.6948000 | 0.0333000 | Freshw. Biol. |
| 86 | 197 | Indraswari et al. 2018 | 2018 | 3.400 | ACI | anurans | T | sound_abundance | acoustic | YES | 33 | 0.3773000 | 0.0333000 | Freshw. Biol. |
| 86 | 198 | Indraswari et al. 2018 | 2018 | 3.400 | Ht | anurans | T | sound_abundance | acoustic | YES | 33 | 0.4426000 | 0.0333000 | Freshw. Biol. |
| 86 | 254 | Indraswari et al. 2018 | 2018 | 3.400 | ACI | anurans | T | sound_abundance | acoustic | YES | 33 | 0.2617000 | 0.0333000 | Freshw. Biol. |
| 86 | 255 | Indraswari et al. 2018 | 2018 | 3.400 | Ht | anurans | T | sound_abundance | acoustic | YES | 33 | 0.4880000 | 0.0333000 | Freshw. Biol. |
| 87 | 96 | Eldridge et al. 2018 | 2018 | 4.490 | ACI | birds | T | richness | acoustic | YES | 4 | 0.6250000 | 0.6000000 | Ecol. Indic. |
| 87 | 98 | Eldridge et al. 2018 | 2018 | 4.490 | ACI | birds | T | sound_abundance | acoustic | YES | 4 | 0.6507600 | 0.6000000 | Ecol. Indic. |
| 87 | 204 | Eldridge et al. 2018 | 2018 | 4.490 | ACI | birds | T | richness | acoustic | YES | 4 | 0.1909000 | 0.6000000 | Ecol. Indic. |
| 87 | 206 | Eldridge et al. 2018 | 2018 | 4.490 | ACI | several | T | sound_abundance | acoustic | YES | 4 | -0.0819200 | 0.6000000 | Ecol. Indic. |
| 87 | 258 | Eldridge et al. 2018 | 2018 | 4.490 | ADI | birds | T | richness | acoustic | YES | 4 | -0.7218000 | 1.0000000 | Ecol. Indic. |
| 87 | 259 | Eldridge et al. 2018 | 2018 | 4.490 | ADI | birds | T | sound_abundance | acoustic | YES | 4 | -0.7547000 | 1.0000000 | Ecol. Indic. |
| 87 | 260 | Eldridge et al. 2018 | 2018 | 4.490 | AEI | birds | T | richness | acoustic | YES | 4 | 0.5061000 | 1.0000000 | Ecol. Indic. |
| 87 | 261 | Eldridge et al. 2018 | 2018 | 4.490 | AEI | birds | T | sound_abundance | acoustic | YES | 4 | 0.8429000 | 1.0000000 | Ecol. Indic. |
| 87 | 296 | Eldridge et al. 2018 | 2018 | 4.490 | ADI | birds | T | richness | acoustic | YES | 4 | -0.2078000 | 1.0000000 | Ecol. Indic. |
| 87 | 297 | Eldridge et al. 2018 | 2018 | 4.490 | ADI | several | T | sound_abundance | acoustic | YES | 4 | -0.5061000 | 1.0000000 | Ecol. Indic. |
| 87 | 298 | Eldridge et al. 2018 | 2018 | 4.490 | AEI | birds | T | richness | acoustic | YES | 4 | 0.2100000 | 1.0000000 | Ecol. Indic. |
| 87 | 299 | Eldridge et al. 2018 | 2018 | 4.490 | AEI | several | T | sound_abundance | acoustic | YES | 4 | 0.5731000 | 1.0000000 | Ecol. Indic. |
| 87 | 309 | Eldridge et al. 2018 | 2018 | 4.490 | BIO | birds | T | richness | acoustic | YES | 4 | 0.8064000 | 1.0000000 | Ecol. Indic. |
| 87 | 310 | Eldridge et al. 2018 | 2018 | 4.490 | BIO | birds | T | sound_abundance | acoustic | YES | 4 | 0.9009000 | 1.0000000 | Ecol. Indic. |
| 87 | 320 | Eldridge et al. 2018 | 2018 | 4.490 | BIO | birds | T | richness | acoustic | YES | 4 | 0.2100000 | 1.0000000 | Ecol. Indic. |
| 87 | 321 | Eldridge et al. 2018 | 2018 | 4.490 | BIO | several | T | sound_abundance | acoustic | YES | 4 | 0.6446000 | 1.0000000 | Ecol. Indic. |
| 87 | 322 | Eldridge et al. 2018 | 2018 | 4.490 | Hf | birds | T | richness | acoustic | YES | 4 | -0.5731000 | 1.0000000 | Ecol. Indic. |
| 87 | 323 | Eldridge et al. 2018 | 2018 | 4.490 | Hf | birds | T | sound_abundance | acoustic | YES | 4 | -0.7218000 | 1.0000000 | Ecol. Indic. |
| 87 | 324 | Eldridge et al. 2018 | 2018 | 4.490 | Ht | birds | T | richness | acoustic | YES | 4 | -0.6595000 | 1.0000000 | Ecol. Indic. |
| 87 | 325 | Eldridge et al. 2018 | 2018 | 4.490 | Ht | birds | T | sound_abundance | acoustic | YES | 4 | -0.6902000 | 1.0000000 | Ecol. Indic. |
| 87 | 335 | Eldridge et al. 2018 | 2018 | 4.490 | Hf | birds | T | richness | acoustic | YES | 4 | -0.1905000 | 1.0000000 | Ecol. Indic. |
| 87 | 336 | Eldridge et al. 2018 | 2018 | 4.490 | Hf | several | T | sound_abundance | acoustic | YES | 4 | -0.6446000 | 1.0000000 | Ecol. Indic. |
| 87 | 337 | Eldridge et al. 2018 | 2018 | 4.490 | Ht | birds | T | richness | acoustic | YES | 4 | -0.1905000 | 1.0000000 | Ecol. Indic. |
| 87 | 338 | Eldridge et al. 2018 | 2018 | 4.490 | Ht | several | T | sound_abundance | acoustic | YES | 4 | -0.3237000 | 1.0000000 | Ecol. Indic. |
| 87 | 348 | Eldridge et al. 2018 | 2018 | 4.490 | NDSI | birds | T | richness | acoustic | YES | 4 | 0.3237000 | 1.0000000 | Ecol. Indic. |
| 87 | 349 | Eldridge et al. 2018 | 2018 | 4.490 | NDSI | birds | T | sound_abundance | acoustic | YES | 4 | 0.4303000 | 1.0000000 | Ecol. Indic. |
| 87 | 359 | Eldridge et al. 2018 | 2018 | 4.490 | NDSI | birds | T | richness | acoustic | YES | 4 | 0.2672000 | 1.0000000 | Ecol. Indic. |
| 87 | 360 | Eldridge et al. 2018 | 2018 | 4.490 | NDSI | several | T | sound_abundance | acoustic | YES | 4 | 0.4303000 | 1.0000000 | Ecol. Indic. |
| 89 | 50 | Gage et al. 2017 | 2017 | 1.820 | ACI | birds | T | richness | acoustic | YES | 60 | 0.6885000 | 0.0175000 | Ecol. Inform. |
| 89 | 51 | Gage et al. 2017 | 2017 | 1.820 | ACI | birds | T | sound_abundance | acoustic | YES | 60 | 1.4618000 | 0.0175000 | Ecol. Inform. |
| 89 | 52 | Gage et al. 2017 | 2017 | 1.820 | ADI | birds | T | richness | acoustic | YES | 60 | -1.8635000 | 0.0175000 | Ecol. Inform. |
| 89 | 53 | Gage et al. 2017 | 2017 | 1.820 | ADI | birds | T | sound_abundance | acoustic | YES | 60 | -0.8852000 | 0.0175000 | Ecol. Inform. |
| 89 | 54 | Gage et al. 2017 | 2017 | 1.820 | AEI | birds | T | richness | acoustic | YES | 60 | 2.2494000 | 0.0175000 | Ecol. Inform. |
| 89 | 55 | Gage et al. 2017 | 2017 | 1.820 | AEI | birds | T | sound_abundance | acoustic | YES | 60 | 1.0302000 | 0.0175000 | Ecol. Inform. |
| 89 | 56 | Gage et al. 2017 | 2017 | 1.820 | BIO | birds | T | richness | acoustic | YES | 60 | -0.6098000 | 0.0175000 | Ecol. Inform. |
| 89 | 57 | Gage et al. 2017 | 2017 | 1.820 | BIO | birds | T | sound_abundance | acoustic | YES | 60 | -0.0993000 | 0.0175000 | Ecol. Inform. |
| 89 | 58 | Gage et al. 2017 | 2017 | 1.820 | H | birds | T | richness | acoustic | YES | 60 | 1.0849000 | 0.0175000 | Ecol. Inform. |
| 89 | 59 | Gage et al. 2017 | 2017 | 1.820 | H | birds | T | sound_abundance | acoustic | YES | 60 | 2.4427000 | 0.0175000 | Ecol. Inform. |
| 89 | 60 | Gage et al. 2017 | 2017 | 1.820 | NDSI | birds | T | richness | acoustic | YES | 60 | 0.0270000 | 0.0175000 | Ecol. Inform. |
| 89 | 61 | Gage et al. 2017 | 2017 | 1.820 | NDSI | birds | T | sound_abundance | acoustic | YES | 60 | 0.5269000 | 0.0175000 | Ecol. Inform. |
| 90 | 104 | Ferreira et al. 2018 | 2018 | NA | ACI | anurans | T | sound_richness | acoustic | NO | 7 | 0.0556000 | 0.2500000 | Journal of ecoacoustics |
| 90 | 108 | Ferreira et al. 2018 | 2018 | NA | ACI | birds | T | sound_richness | acoustic | NO | 7 | -0.1125000 | 0.2500000 | Journal of ecoacoustics |
| 90 | 109 | Ferreira et al. 2018 | 2018 | NA | ACI | invertebrates | T | sound_richness | acoustic | NO | 7 | 0.1765000 | 0.2500000 | Journal of ecoacoustics |
| 90 | 110 | Ferreira et al. 2018 | 2018 | NA | ACI | mammals | T | sound_richness | acoustic | NO | 7 | -0.0598000 | 0.2500000 | Journal of ecoacoustics |
| 90 | 111 | Ferreira et al. 2018 | 2018 | NA | ADI | anurans | T | sound_richness | acoustic | NO | 7 | 1.0277000 | 0.2500000 | Journal of ecoacoustics |
| 90 | 115 | Ferreira et al. 2018 | 2018 | NA | ADI | birds | T | sound_richness | acoustic | NO | 7 | -0.4747000 | 0.2500000 | Journal of ecoacoustics |
| 90 | 116 | Ferreira et al. 2018 | 2018 | NA | ADI | invertebrates | T | sound_richness | acoustic | NO | 7 | 0.8162000 | 0.2500000 | Journal of ecoacoustics |
| 90 | 117 | Ferreira et al. 2018 | 2018 | NA | ADI | mammals | T | sound_richness | acoustic | NO | 7 | 0.3826000 | 0.2500000 | Journal of ecoacoustics |
| 90 | 118 | Ferreira et al. 2018 | 2018 | NA | AEI | anurans | T | sound_richness | acoustic | NO | 7 | -0.9660000 | 0.2500000 | Journal of ecoacoustics |
| 90 | 122 | Ferreira et al. 2018 | 2018 | NA | AEI | birds | T | sound_richness | acoustic | NO | 7 | 0.6011000 | 0.2500000 | Journal of ecoacoustics |
| 90 | 123 | Ferreira et al. 2018 | 2018 | NA | AEI | invertebrates | T | sound_richness | acoustic | NO | 7 | -0.7058000 | 0.2500000 | Journal of ecoacoustics |
| 90 | 124 | Ferreira et al. 2018 | 2018 | NA | AEI | mammals | T | sound_richness | acoustic | NO | 7 | -0.4389000 | 0.2500000 | Journal of ecoacoustics |
| 90 | 125 | Ferreira et al. 2018 | 2018 | NA | BIO | anurans | T | sound_richness | acoustic | NO | 7 | -0.1743000 | 0.2500000 | Journal of ecoacoustics |
| 90 | 129 | Ferreira et al. 2018 | 2018 | NA | BIO | birds | T | sound_richness | acoustic | NO | 7 | -0.1358000 | 0.2500000 | Journal of ecoacoustics |
| 90 | 130 | Ferreira et al. 2018 | 2018 | NA | BIO | invertebrates | T | sound_richness | acoustic | NO | 7 | -0.0556000 | 0.2500000 | Journal of ecoacoustics |
| 90 | 131 | Ferreira et al. 2018 | 2018 | NA | BIO | mammals | T | sound_richness | acoustic | NO | 7 | -0.0839000 | 0.2500000 | Journal of ecoacoustics |
| 90 | 132 | Ferreira et al. 2018 | 2018 | NA | H | anurans | T | sound_richness | acoustic | NO | 7 | 1.2090000 | 0.2500000 | Journal of ecoacoustics |
| 90 | 136 | Ferreira et al. 2018 | 2018 | NA | H | birds | T | sound_richness | acoustic | NO | 7 | -0.5048000 | 0.2500000 | Journal of ecoacoustics |
| 90 | 137 | Ferreira et al. 2018 | 2018 | NA | H | invertebrates | T | sound_richness | acoustic | NO | 7 | 0.8949000 | 0.2500000 | Journal of ecoacoustics |
| 90 | 138 | Ferreira et al. 2018 | 2018 | NA | H | mammals | T | sound_richness | acoustic | NO | 7 | 0.4169000 | 0.2500000 | Journal of ecoacoustics |
| 90 | 142 | Ferreira et al. 2018 | 2018 | NA | NDSI | anurans | T | sound_richness | acoustic | NO | 7 | 1.1933000 | 0.2500000 | Journal of ecoacoustics |
| 90 | 146 | Ferreira et al. 2018 | 2018 | NA | NDSI | birds | T | sound_richness | acoustic | NO | 7 | -0.5567000 | 0.2500000 | Journal of ecoacoustics |
| 90 | 147 | Ferreira et al. 2018 | 2018 | NA | NDSI | invertebrates | T | sound_richness | acoustic | NO | 7 | 0.8485000 | 0.2500000 | Journal of ecoacoustics |
| 90 | 148 | Ferreira et al. 2018 | 2018 | NA | NDSI | mammals | T | sound_richness | acoustic | NO | 7 | 0.4513000 | 0.2500000 | Journal of ecoacoustics |
| 92 | 88 | Torti et al. 2018 | 2018 | 1.950 | ACI | mammals | T | abundance | no_acoustic | NO | 258 | 0.6150000 | 0.0039000 | |
| 92 | 89 | Torti et al. 2018 | 2018 | 1.950 | ADI | mammals | T | abundance | no_acoustic | NO | 258 | 0.0174000 | 0.0039000 | |
| 92 | 90 | Torti et al. 2018 | 2018 | 1.950 | AR | mammals | T | abundance | no_acoustic | NO | 258 | 0.0266000 | 0.0039000 | |
| 92 | 91 | Torti et al. 2018 | 2018 | 1.950 | H | mammals | T | abundance | no_acoustic | NO | 258 | 0.1404000 | 0.0039000 | |
| 96 | 105 | Izaguirre et al. 2018 | 2018 | NA | ACI | birds | T | abundance | no_acoustic | YES | 12 | 0.7315000 | 0.1111000 | Journal of ecoacoustics |
| 96 | 106 | Izaguirre et al. 2018 | 2018 | NA | ACI | birds | T | diversity | no_acoustic | YES | 12 | -0.7068500 | 0.0833250 | Journal of ecoacoustics |
| 96 | 107 | Izaguirre et al. 2018 | 2018 | NA | ACI | birds | T | richness | no_acoustic | YES | 12 | -0.7941000 | 0.1111000 | Journal of ecoacoustics |
| 96 | 112 | Izaguirre et al. 2018 | 2018 | NA | ADI | birds | T | abundance | no_acoustic | YES | 12 | -0.7958000 | 0.1111000 | Journal of ecoacoustics |
| 96 | 113 | Izaguirre et al. 2018 | 2018 | NA | ADI | birds | T | diversity | no_acoustic | YES | 12 | 0.6084000 | 0.0833250 | Journal of ecoacoustics |
| 96 | 114 | Izaguirre et al. 2018 | 2018 | NA | ADI | birds | T | richness | no_acoustic | YES | 12 | 0.5192000 | 0.1111000 | Journal of ecoacoustics |
| 96 | 119 | Izaguirre et al. 2018 | 2018 | NA | AEI | birds | T | abundance | no_acoustic | YES | 12 | 0.7430000 | 0.1111000 | Journal of ecoacoustics |
| 96 | 120 | Izaguirre et al. 2018 | 2018 | NA | AEI | birds | T | diversity | no_acoustic | YES | 12 | -0.5394000 | 0.0833250 | Journal of ecoacoustics |
| 96 | 121 | Izaguirre et al. 2018 | 2018 | NA | AEI | birds | T | richness | no_acoustic | YES | 12 | -0.4181000 | 0.1111000 | Journal of ecoacoustics |
| 96 | 126 | Izaguirre et al. 2018 | 2018 | NA | BIO | birds | T | abundance | no_acoustic | YES | 12 | 0.5553000 | 0.1111000 | Journal of ecoacoustics |
| 96 | 127 | Izaguirre et al. 2018 | 2018 | NA | BIO | birds | T | diversity | no_acoustic | YES | 12 | -0.6801500 | 0.0833250 | Journal of ecoacoustics |
| 96 | 128 | Izaguirre et al. 2018 | 2018 | NA | BIO | birds | T | richness | no_acoustic | YES | 12 | -0.5485000 | 0.1111000 | Journal of ecoacoustics |
| 96 | 133 | Izaguirre et al. 2018 | 2018 | NA | H | birds | T | abundance | no_acoustic | YES | 12 | -0.3904000 | 0.1111000 | Journal of ecoacoustics |
| 96 | 134 | Izaguirre et al. 2018 | 2018 | NA | H | birds | T | diversity | no_acoustic | YES | 12 | 0.3747000 | 0.0833250 | Journal of ecoacoustics |
| 96 | 135 | Izaguirre et al. 2018 | 2018 | NA | H | birds | T | richness | no_acoustic | YES | 12 | 0.5773000 | 0.1111000 | Journal of ecoacoustics |
| 96 | 139 | Izaguirre et al. 2018 | 2018 | NA | M | birds | T | abundance | no_acoustic | YES | 12 | 0.4944000 | 0.1111000 | Journal of ecoacoustics |
| 96 | 140 | Izaguirre et al. 2018 | 2018 | NA | M | birds | T | diversity | no_acoustic | YES | 12 | -0.6604000 | 0.0833250 | Journal of ecoacoustics |
| 96 | 141 | Izaguirre et al. 2018 | 2018 | NA | M | birds | T | richness | no_acoustic | YES | 12 | -0.6686000 | 0.1111000 | Journal of ecoacoustics |
| 96 | 143 | Izaguirre et al. 2018 | 2018 | NA | NDSI | birds | T | abundance | no_acoustic | YES | 12 | -0.0955000 | 0.1111000 | Journal of ecoacoustics |
| 96 | 144 | Izaguirre et al. 2018 | 2018 | NA | NDSI | birds | T | diversity | no_acoustic | YES | 12 | 0.2976500 | 0.0833250 | Journal of ecoacoustics |
| 96 | 145 | Izaguirre et al. 2018 | 2018 | NA | NDSI | birds | T | richness | no_acoustic | YES | 12 | 0.4012000 | 0.1111000 | Journal of ecoacoustics |
| 96 | 149 | Izaguirre et al. 2018 | 2018 | NA | NP | birds | T | abundance | no_acoustic | YES | 12 | -0.5983000 | 0.1111000 | Journal of ecoacoustics |
| 96 | 150 | Izaguirre et al. 2018 | 2018 | NA | NP | birds | T | diversity | no_acoustic | YES | 12 | 0.6247500 | 0.0833250 | Journal of ecoacoustics |
| 96 | 151 | Izaguirre et al. 2018 | 2018 | NA | NP | birds | T | richness | no_acoustic | YES | 12 | 0.6372000 | 0.1111000 | Journal of ecoacoustics |
| 251 | 168 | Buxton et al. 2016 | 2016 | 2.440 | ACI | birds | T | diversity | acoustic | NO | 72 | 0.2300000 | 0.0108750 | Ecol. Evol. |
| 251 | 169 | Buxton et al. 2016 | 2016 | 2.440 | ACI | birds | T | richness | acoustic | NO | 72 | 0.3673000 | 0.0145000 | Ecol. Evol. |
| 427 | 27 | Fuller et al. 2015 | 2015 | 3.190 | ACI | birds | T | richness | acoustic | NO | 380 | 0.0503000 | 0.0027000 | Ecol. Indic. |
| 427 | 28 | Fuller et al. 2015 | 2015 | 3.190 | ADI | birds | T | richness | acoustic | NO | 380 | 0.0395000 | 0.0027000 | Ecol. Indic. |
| 427 | 29 | Fuller et al. 2015 | 2015 | 3.190 | AEI | birds | T | richness | acoustic | NO | 380 | 0.0864000 | 0.0027000 | Ecol. Indic. |
| 427 | 30 | Fuller et al. 2015 | 2015 | 3.190 | BIO | birds | T | richness | acoustic | NO | 380 | 0.0543000 | 0.0027000 | Ecol. Indic. |
| 427 | 31 | Fuller et al. 2015 | 2015 | 3.190 | H | birds | T | richness | acoustic | NO | 380 | 0.1281000 | 0.0027000 | Ecol. Indic. |
| 427 | 32 | Fuller et al. 2015 | 2015 | 3.190 | NDSI | birds | T | richness | acoustic | NO | 380 | 0.1517000 | 0.0027000 | Ecol. Indic. |
| 1132 | 10 | Depraetere et al. 2012 | 2012 | 2.890 | AR | birds | T | richness | acoustic | NO | 12 | 1.7295000 | 0.1111000 | Ecol. Indic. |
| 1177 | 5 | Joo et al. 2011 | 2011 | 2.170 | H | birds | T | richness | acoustic | YES | 5 | 0.1820000 | 0.5000000 | Landsc. Urban Plan. |
| 1262 | 6 | Pieretti et al. 2011 | 2011 | 2.700 | ACI | birds | T | sound_abundance | acoustic | YES | 20 | 1.2568600 | 0.0352800 | Ecol. Indic. |
| 2740 | 69 | Mammides et al. 2017 | 2017 | 3.980 | ACI | birds | T | diversity | no_acoustic | NO | 47 | 0.1614000 | 0.0227000 | Ecol. Indic. |
| 2740 | 70 | Mammides et al. 2017 | 2017 | 3.980 | ACI | birds | T | richness | no_acoustic | NO | 47 | 0.2132000 | 0.0227000 | Ecol. Indic. |
| 2740 | 72 | Mammides et al. 2017 | 2017 | 3.980 | ADI | birds | T | diversity | no_acoustic | NO | 47 | 0.3769000 | 0.0227000 | Ecol. Indic. |
| 2740 | 73 | Mammides et al. 2017 | 2017 | 3.980 | ADI | birds | T | richness | no_acoustic | NO | 47 | 0.3654000 | 0.0227000 | Ecol. Indic. |
| 2740 | 74 | Mammides et al. 2017 | 2017 | 3.980 | AEI | birds | T | diversity | no_acoustic | NO | 47 | -0.4118000 | 0.0227000 | Ecol. Indic. |
| 2740 | 75 | Mammides et al. 2017 | 2017 | 3.980 | AEI | birds | T | richness | no_acoustic | NO | 47 | -0.4236000 | 0.0227000 | Ecol. Indic. |
| 2740 | 76 | Mammides et al. 2017 | 2017 | 3.980 | AR | birds | T | diversity | no_acoustic | NO | 47 | -0.4847000 | 0.0227000 | Ecol. Indic. |
| 2740 | 77 | Mammides et al. 2017 | 2017 | 3.980 | AR | birds | T | richness | no_acoustic | NO | 47 | -0.4973000 | 0.0227000 | Ecol. Indic. |
| 2740 | 78 | Mammides et al. 2017 | 2017 | 3.980 | BIO | birds | T | diversity | no_acoustic | NO | 47 | -0.2986000 | 0.0227000 | Ecol. Indic. |
| 2740 | 79 | Mammides et al. 2017 | 2017 | 3.980 | BIO | birds | T | richness | no_acoustic | NO | 47 | -0.3541000 | 0.0227000 | Ecol. Indic. |
| 2740 | 81 | Mammides et al. 2017 | 2017 | 3.980 | H | birds | T | diversity | no_acoustic | NO | 47 | 0.5361000 | 0.0227000 | Ecol. Indic. |
| 2740 | 82 | Mammides et al. 2017 | 2017 | 3.980 | H | birds | T | richness | no_acoustic | NO | 47 | 0.5627000 | 0.0227000 | Ecol. Indic. |
| 2740 | 83 | Mammides et al. 2017 | 2017 | 3.980 | NDSI | birds | T | diversity | no_acoustic | NO | 47 | -0.0100000 | 0.0227000 | Ecol. Indic. |
| 2740 | 84 | Mammides et al. 2017 | 2017 | 3.980 | NDSI | birds | T | richness | no_acoustic | NO | 47 | -0.0100000 | 0.0227000 | Ecol. Indic. |
| 2740 | 182 | Mammides et al. 2017 | 2017 | 3.980 | ACI | birds | T | diversity | no_acoustic | NO | 47 | 0.0601000 | 0.0227000 | Ecol. Indic. |
| 2740 | 183 | Mammides et al. 2017 | 2017 | 3.980 | ACI | birds | T | richness | no_acoustic | NO | 47 | -0.0300000 | 0.0227000 | Ecol. Indic. |
| 2740 | 184 | Mammides et al. 2017 | 2017 | 3.980 | ADI | birds | T | diversity | no_acoustic | NO | 47 | 0.7250000 | 0.0227000 | Ecol. Indic. |
| 2740 | 185 | Mammides et al. 2017 | 2017 | 3.980 | ADI | birds | T | richness | no_acoustic | NO | 47 | 0.6184000 | 0.0227000 | Ecol. Indic. |
| 2740 | 187 | Mammides et al. 2017 | 2017 | 3.980 | AEI | birds | T | diversity | no_acoustic | NO | 47 | -0.7089000 | 0.0227000 | Ecol. Indic. |
| 2740 | 188 | Mammides et al. 2017 | 2017 | 3.980 | AEI | birds | T | richness | no_acoustic | NO | 47 | -0.6042000 | 0.0227000 | Ecol. Indic. |
| 2740 | 189 | Mammides et al. 2017 | 2017 | 3.980 | AR | birds | T | diversity | no_acoustic | NO | 47 | -0.2448000 | 0.0227000 | Ecol. Indic. |
| 2740 | 190 | Mammides et al. 2017 | 2017 | 3.980 | AR | birds | T | richness | no_acoustic | NO | 47 | -0.2448000 | 0.0227000 | Ecol. Indic. |
| 2740 | 191 | Mammides et al. 2017 | 2017 | 3.980 | BIO | birds | T | diversity | no_acoustic | NO | 47 | 0.2027000 | 0.0227000 | Ecol. Indic. |
| 2740 | 192 | Mammides et al. 2017 | 2017 | 3.980 | BIO | birds | T | richness | no_acoustic | NO | 47 | 0.2342000 | 0.0227000 | Ecol. Indic. |
| 2740 | 193 | Mammides et al. 2017 | 2017 | 3.980 | H | birds | T | diversity | no_acoustic | NO | 47 | 0.7928000 | 0.0227000 | Ecol. Indic. |
| 2740 | 194 | Mammides et al. 2017 | 2017 | 3.980 | H | birds | T | richness | no_acoustic | NO | 47 | 0.6777000 | 0.0227000 | Ecol. Indic. |
| 2740 | 195 | Mammides et al. 2017 | 2017 | 3.980 | NDSI | birds | T | diversity | no_acoustic | NO | 47 | 0.6931000 | 0.0227000 | Ecol. Indic. |
| 2740 | 196 | Mammides et al. 2017 | 2017 | 3.980 | NDSI | birds | T | richness | no_acoustic | NO | 47 | 0.6042000 | 0.0227000 | Ecol. Indic. |
| 2740 | 240 | Mammides et al. 2017 | 2017 | 3.980 | ACI | birds | T | diversity | no_acoustic | NO | 50 | 0.0601000 | 0.0213000 | Ecol. Indic. |
| 2740 | 241 | Mammides et al. 2017 | 2017 | 3.980 | ACI | birds | T | richness | no_acoustic | NO | 50 | 0.0400000 | 0.0213000 | Ecol. Indic. |
| 2740 | 242 | Mammides et al. 2017 | 2017 | 3.980 | ADI | birds | T | diversity | no_acoustic | NO | 50 | 0.5101000 | 0.0213000 | Ecol. Indic. |
| 2740 | 243 | Mammides et al. 2017 | 2017 | 3.980 | ADI | birds | T | richness | no_acoustic | NO | 50 | 0.6328000 | 0.0213000 | Ecol. Indic. |
| 2740 | 244 | Mammides et al. 2017 | 2017 | 3.980 | AEI | birds | T | diversity | no_acoustic | NO | 50 | -0.4973000 | 0.0213000 | Ecol. Indic. |
| 2740 | 245 | Mammides et al. 2017 | 2017 | 3.980 | AEI | birds | T | richness | no_acoustic | NO | 50 | -0.6475000 | 0.0213000 | Ecol. Indic. |
| 2740 | 246 | Mammides et al. 2017 | 2017 | 3.980 | AR | birds | T | diversity | no_acoustic | NO | 50 | -0.1003000 | 0.0213000 | Ecol. Indic. |
| 2740 | 247 | Mammides et al. 2017 | 2017 | 3.980 | AR | birds | T | richness | no_acoustic | NO | 50 | -0.0802000 | 0.0213000 | Ecol. Indic. |
| 2740 | 248 | Mammides et al. 2017 | 2017 | 3.980 | BIO | birds | T | diversity | no_acoustic | NO | 50 | 0.2237000 | 0.0213000 | Ecol. Indic. |
| 2740 | 249 | Mammides et al. 2017 | 2017 | 3.980 | BIO | birds | T | richness | no_acoustic | NO | 50 | 0.3884000 | 0.0213000 | Ecol. Indic. |
| 2740 | 250 | Mammides et al. 2017 | 2017 | 3.980 | H | birds | T | diversity | no_acoustic | NO | 50 | 0.3095000 | 0.0213000 | Ecol. Indic. |
| 2740 | 251 | Mammides et al. 2017 | 2017 | 3.980 | H | birds | T | richness | no_acoustic | NO | 50 | 0.3769000 | 0.0213000 | Ecol. Indic. |
| 2740 | 252 | Mammides et al. 2017 | 2017 | 3.980 | NDSI | birds | T | diversity | no_acoustic | NO | 50 | 0.2769000 | 0.0213000 | Ecol. Indic. |
| 2740 | 253 | Mammides et al. 2017 | 2017 | 3.980 | NDSI | birds | T | richness | no_acoustic | NO | 50 | 0.3654000 | 0.0213000 | Ecol. Indic. |
| 2740 | 287 | Mammides et al. 2017 | 2017 | 3.980 | ACI | birds | T | richness | no_acoustic | NO | 10 | 0.0701000 | 0.1429000 | Ecol. Indic. |
| 2740 | 288 | Mammides et al. 2017 | 2017 | 3.980 | ADI | birds | T | richness | no_acoustic | NO | 10 | 0.6042000 | 0.1429000 | Ecol. Indic. |
| 2740 | 289 | Mammides et al. 2017 | 2017 | 3.980 | AEI | birds | T | richness | no_acoustic | NO | 10 | -0.6625000 | 0.1429000 | Ecol. Indic. |
| 2740 | 290 | Mammides et al. 2017 | 2017 | 3.980 | AR | birds | T | richness | no_acoustic | NO | 10 | -0.1003000 | 0.1429000 | Ecol. Indic. |
| 2740 | 291 | Mammides et al. 2017 | 2017 | 3.980 | BIO | birds | T | richness | no_acoustic | NO | 10 | 0.1923000 | 0.1429000 | Ecol. Indic. |
| 2740 | 292 | Mammides et al. 2017 | 2017 | 3.980 | H | birds | T | richness | no_acoustic | NO | 10 | 0.4236000 | 0.1429000 | Ecol. Indic. |
| 2740 | 293 | Mammides et al. 2017 | 2017 | 3.980 | NDSI | birds | T | richness | no_acoustic | NO | 10 | 0.3769000 | 0.1429000 | Ecol. Indic. |
| 2740 | 302 | Mammides et al. 2017 | 2017 | 3.980 | ACI | birds | T | richness | no_acoustic | NO | 10 | 0.0300000 | 0.1429000 | Ecol. Indic. |
| 2740 | 303 | Mammides et al. 2017 | 2017 | 3.980 | ADI | birds | T | richness | no_acoustic | NO | 10 | 0.6475000 | 0.1429000 | Ecol. Indic. |
| 2740 | 304 | Mammides et al. 2017 | 2017 | 3.980 | AEI | birds | T | richness | no_acoustic | NO | 10 | -0.6777000 | 0.1429000 | Ecol. Indic. |
| 2740 | 305 | Mammides et al. 2017 | 2017 | 3.980 | AR | birds | T | richness | no_acoustic | NO | 10 | -0.1003000 | 0.1429000 | Ecol. Indic. |
| 2740 | 306 | Mammides et al. 2017 | 2017 | 3.980 | BIO | birds | T | richness | no_acoustic | NO | 10 | 0.2237000 | 0.1429000 | Ecol. Indic. |
| 2740 | 307 | Mammides et al. 2017 | 2017 | 3.980 | H | birds | T | richness | no_acoustic | NO | 10 | 0.4722000 | 0.1429000 | Ecol. Indic. |
| 2740 | 308 | Mammides et al. 2017 | 2017 | 3.980 | NDSI | birds | T | richness | no_acoustic | NO | 10 | 0.4847000 | 0.1429000 | Ecol. Indic. |
| 2740 | 313 | Mammides et al. 2017 | 2017 | 3.980 | ACI | birds | T | richness | no_acoustic | NO | 10 | 0.0701000 | 0.1429000 | Ecol. Indic. |
| 2740 | 314 | Mammides et al. 2017 | 2017 | 3.980 | ADI | birds | T | richness | no_acoustic | NO | 10 | 0.6184000 | 0.1429000 | Ecol. Indic. |
| 2740 | 315 | Mammides et al. 2017 | 2017 | 3.980 | AEI | birds | T | richness | no_acoustic | NO | 10 | -0.6777000 | 0.1429000 | Ecol. Indic. |
| 2740 | 316 | Mammides et al. 2017 | 2017 | 3.980 | AR | birds | T | richness | no_acoustic | NO | 10 | -0.0601000 | 0.1429000 | Ecol. Indic. |
| 2740 | 317 | Mammides et al. 2017 | 2017 | 3.980 | BIO | birds | T | richness | no_acoustic | NO | 10 | 0.2237000 | 0.1429000 | Ecol. Indic. |
| 2740 | 318 | Mammides et al. 2017 | 2017 | 3.980 | H | birds | T | richness | no_acoustic | NO | 10 | 0.4118000 | 0.1429000 | Ecol. Indic. |
| 2740 | 319 | Mammides et al. 2017 | 2017 | 3.980 | NDSI | birds | T | richness | no_acoustic | NO | 10 | 0.3316000 | 0.1429000 | Ecol. Indic. |
| 2740 | 328 | Mammides et al. 2017 | 2017 | 3.980 | ACI | birds | T | richness | no_acoustic | NO | 10 | 0.0300000 | 0.1429000 | Ecol. Indic. |
| 2740 | 329 | Mammides et al. 2017 | 2017 | 3.980 | ADI | birds | T | richness | no_acoustic | NO | 10 | 0.6184000 | 0.1429000 | Ecol. Indic. |
| 2740 | 330 | Mammides et al. 2017 | 2017 | 3.980 | AEI | birds | T | richness | no_acoustic | NO | 10 | -0.6777000 | 0.1429000 | Ecol. Indic. |
| 2740 | 331 | Mammides et al. 2017 | 2017 | 3.980 | AR | birds | T | richness | no_acoustic | NO | 10 | -0.0601000 | 0.1429000 | Ecol. Indic. |
| 2740 | 332 | Mammides et al. 2017 | 2017 | 3.980 | BIO | birds | T | richness | no_acoustic | NO | 10 | 0.2342000 | 0.1429000 | Ecol. Indic. |
| 2740 | 333 | Mammides et al. 2017 | 2017 | 3.980 | H | birds | T | richness | no_acoustic | NO | 10 | 0.4236000 | 0.1429000 | Ecol. Indic. |
| 2740 | 334 | Mammides et al. 2017 | 2017 | 3.980 | NDSI | birds | T | richness | no_acoustic | NO | 10 | 0.3884000 | 0.1429000 | Ecol. Indic. |
| 2740 | 341 | Mammides et al. 2017 | 2017 | 3.980 | ACI | birds | T | richness | no_acoustic | NO | 10 | 0.1104000 | 0.1429000 | Ecol. Indic. |
| 2740 | 342 | Mammides et al. 2017 | 2017 | 3.980 | ADI | birds | T | richness | no_acoustic | NO | 10 | 0.6042000 | 0.1429000 | Ecol. Indic. |
| 2740 | 343 | Mammides et al. 2017 | 2017 | 3.980 | AEI | birds | T | richness | no_acoustic | NO | 10 | -0.6475000 | 0.1429000 | Ecol. Indic. |
| 2740 | 344 | Mammides et al. 2017 | 2017 | 3.980 | AR | birds | T | richness | no_acoustic | NO | 10 | -0.0701000 | 0.1429000 | Ecol. Indic. |
| 2740 | 345 | Mammides et al. 2017 | 2017 | 3.980 | BIO | birds | T | richness | no_acoustic | NO | 10 | 0.2342000 | 0.1429000 | Ecol. Indic. |
| 2740 | 346 | Mammides et al. 2017 | 2017 | 3.980 | H | birds | T | richness | no_acoustic | NO | 10 | 0.4236000 | 0.1429000 | Ecol. Indic. |
| 2740 | 347 | Mammides et al. 2017 | 2017 | 3.980 | NDSI | birds | T | richness | no_acoustic | NO | 10 | 0.4477000 | 0.1429000 | Ecol. Indic. |
| 2740 | 352 | Mammides et al. 2017 | 2017 | 3.980 | ACI | birds | T | richness | no_acoustic | NO | 10 | 0.0902000 | 0.1429000 | Ecol. Indic. |
| 2740 | 353 | Mammides et al. 2017 | 2017 | 3.980 | ADI | birds | T | richness | no_acoustic | NO | 10 | 0.6328000 | 0.1429000 | Ecol. Indic. |
| 2740 | 354 | Mammides et al. 2017 | 2017 | 3.980 | AEI | birds | T | richness | no_acoustic | NO | 10 | -0.6777000 | 0.1429000 | Ecol. Indic. |
| 2740 | 355 | Mammides et al. 2017 | 2017 | 3.980 | AR | birds | T | richness | no_acoustic | NO | 10 | -0.1003000 | 0.1429000 | Ecol. Indic. |
| 2740 | 356 | Mammides et al. 2017 | 2017 | 3.980 | BIO | birds | T | richness | no_acoustic | NO | 10 | 0.2027000 | 0.1429000 | Ecol. Indic. |
| 2740 | 357 | Mammides et al. 2017 | 2017 | 3.980 | H | birds | T | richness | no_acoustic | NO | 10 | 0.4599000 | 0.1429000 | Ecol. Indic. |
| 2740 | 358 | Mammides et al. 2017 | 2017 | 3.980 | NDSI | birds | T | richness | no_acoustic | NO | 10 | 0.4356000 | 0.1429000 | Ecol. Indic. |
| 2745 | 2 | Sueur et al. 2008 | 2008 | 4.810 | H | several | T | richness | acoustic | NO | 10 | 1.7211000 | 0.1429000 | PLoS One |
| 2977 | 97 | Jorge et al. 2018 | 2018 | 4.490 | ACI | birds | T | richness | acoustic | YES | 9 | 0.5731000 | 0.1667000 | Ecol. Indic. |
| 2977 | 99 | Jorge et al. 2018 | 2018 | 4.490 | ADI | birds | T | richness | acoustic | YES | 9 | -0.2672000 | 0.1667000 | Ecol. Indic. |
| 2977 | 100 | Jorge et al. 2018 | 2018 | 4.490 | AEI | birds | T | richness | acoustic | YES | 9 | 0.3123000 | 0.1667000 | Ecol. Indic. |
| 2977 | 101 | Jorge et al. 2018 | 2018 | 4.490 | BIO | birds | T | richness | acoustic | YES | 9 | 0.4181000 | 0.1667000 | Ecol. Indic. |
| 2977 | 102 | Jorge et al. 2018 | 2018 | 4.490 | H | birds | T | richness | acoustic | YES | 9 | -0.1475000 | 0.1667000 | Ecol. Indic. |
| 2977 | 103 | Jorge et al. 2018 | 2018 | 4.490 | NDSI | birds | T | richness | acoustic | YES | 9 | 0.3352000 | 0.1667000 | Ecol. Indic. |
| 2977 | 205 | Jorge et al. 2018 | 2018 | 4.490 | ACI | birds | T | richness | acoustic | YES | 9 | 0.3123000 | 0.1667000 | Ecol. Indic. |
| 2977 | 207 | Jorge et al. 2018 | 2018 | 4.490 | ADI | birds | T | richness | acoustic | YES | 9 | -0.4676000 | 0.1667000 | Ecol. Indic. |
| 2977 | 208 | Jorge et al. 2018 | 2018 | 4.490 | AEI | birds | T | richness | acoustic | YES | 9 | 0.5192000 | 0.1667000 | Ecol. Indic. |
| 2977 | 209 | Jorge et al. 2018 | 2018 | 4.490 | BIO | birds | T | richness | acoustic | YES | 9 | 0.3009000 | 0.1667000 | Ecol. Indic. |
| 2977 | 210 | Jorge et al. 2018 | 2018 | 4.490 | H | birds | T | richness | acoustic | YES | 9 | -0.1582000 | 0.1667000 | Ecol. Indic. |
| 2977 | 211 | Jorge et al. 2018 | 2018 | 4.490 | NDSI | birds | T | richness | acoustic | YES | 9 | 0.3352000 | 0.1667000 | Ecol. Indic. |
| 2986 | 68 | Raynor et al. 2017 | 2017 | 2.720 | ACI | birds | T | richness | acoustic | YES | 6 | 0.2448000 | 0.3333000 | Condor |
Table S2 - Variable descriptions for Table S1.
vd <- data.frame(Variables = c("id", "entry", "journal", "year", "impact_factor", "index",
"taxa", "environ", "bio", "diversity_source", "pseudoreplication",
"n", "z", "var"),
Descriptions = c(
"Identification number for the study",
"Identification number for the effect size (row entry)",
"Journal where the study was published",
"Year of publication",
"Impact factor of the Journal",
"Acoustic index used",
"Primary studied group (invertebrates, fish, anurans, birds, mammals or several)",
"Ecosystem type where recordings were collected (A for Aquatic, T for terrestrial)",
"Diversity metric used to correlate with acoustic index values",
paste("Source of biological data. ",
"Sound recordings (coded as acoustic) or other source, ",
"i.e. literature, field surveys, etc. (coded as no_acoustic)."),
paste("Pseudoreplicated design to compute acoustic indices",
"relation with biodiversity (coded as YES). Coded as NO otherwise."),
"Sample size adjusted for what was considered the number of true replicates",
"Fisher's Z effect size",
"Fisher's Z variance"
)
)
pander(vd, justify = "left")| Variables | Descriptions |
|---|---|
| id | Identification number for the study |
| entry | Identification number for the effect size (row entry) |
| journal | Journal where the study was published |
| year | Year of publication |
| impact_factor | Impact factor of the Journal |
| index | Acoustic index used |
| taxa | Primary studied group (invertebrates, fish, anurans, birds, mammals or several) |
| environ | Ecosystem type where recordings were collected (A for Aquatic, T for terrestrial) |
| bio | Diversity metric used to correlate with acoustic index values |
| diversity_source | Source of biological data. Sound recordings (coded as acoustic) or other source, i.e. literature, field surveys, etc. (coded as no_acoustic). |
| pseudoreplication | Pseudoreplicated design to compute acoustic indices relation with biodiversity (coded as YES). Coded as NO otherwise. |
| n | Sample size adjusted for what was considered the number of true replicates |
| z | Fisher’s Z effect size |
| var | Fisher’s Z variance |
In what follows, we give a brief overview of the dataset mostly by way of tables and figures.
Our dataset comprised a total of 34 studies and 364 effect sizes. Therefore, most studies contributed with more than one effect size for the meta-analysis.
Table S3 - Number of effect sizes collected from each of the 34 studies included in the meta-analysis. ID corresponds to the study identification number in our dataset.
studies_n <- as.data.frame(table(df_tidy$id), stringsAsFactors = FALSE)
studies_n <- cbind(unique(df_tidy$authors), studies_n)
colnames(studies_n) <- c("Study", "ID", "Effect_sizes")
studies_n <- studies_n %>%
select(ID, Study, Effect_sizes) %>%
arrange(-Effect_sizes)
kable(studies_n, format = "html") %>%
kable_styling(c("striped"))| ID | Study | Effect_sizes |
|---|---|---|
| 2740 | Mammides et al. 2017 | 84 |
| 53 | Moreno-Gomez 2019 | 42 |
| 87 | Eldridge et al. 2018 | 28 |
| 80 | Staaterman et al. 2017 | 24 |
| 90 | Ferreira et al. 2018 | 24 |
| 96 | Izaguirre et al. 2018 | 24 |
| 10 | Buscaino et al. 2016 | 22 |
| 2 | Desjonquères et al. 2015 | 12 |
| 11 | Bertucci et al. 2016 | 12 |
| 89 | Gage et al. 2017 | 12 |
| 2977 | Jorge et al. 2018 | 12 |
| 70 | Bolgan et al. 2018 | 11 |
| 9 | Harris et al. 2016 | 6 |
| 86 | Indraswari et al. 2018 | 6 |
| 427 | Fuller et al. 2015 | 6 |
| 14 | Wa Maina et al. 2016 | 4 |
| 60 | Patrick Lyon et al. 2019 | 4 |
| 77 | Fairbrass et al. 2017 | 4 |
| 92 | Torti et al. 2018 | 4 |
| 15 | Roca & Proulx 2016 | 3 |
| 45 | McLaren 2012 | 3 |
| 13 | McWilliam & Hawkin 2013 | 2 |
| 41 | Paisley-Jones 2011 | 2 |
| 44 | Machado et al. 2017 | 2 |
| 251 | Buxton et al. 2016 | 2 |
| 4 | Parks et al. 2014 | 1 |
| 6 | Boelman et al. 2007 | 1 |
| 17 | Zhang et al. 2015 | 1 |
| 37 | Picciulin et al. 2016 | 1 |
| 1132 | Depraetere et al. 2012 | 1 |
| 1177 | Joo et al. 2011 | 1 |
| 1262 | Pieretti et al. 2011 | 1 |
| 2745 | Sueur et al. 2008 | 1 |
| 2986 | Raynor et al. 2017 | 1 |
The most studied acoustic index was ACI and the most used biodiversity metric was ‘species richness’.
Table S4 - Number of effect sizes and studies per moderator levels.
# Table for moderator levels
mods <- c("index", "bio", "diversity_source", "environ")
sample_sizes <- do.call(rbind, lapply(mods, function(x) n_studies_entries(df_tidy, x)))
# Format output
sample_sizes <- cbind(row.names(sample_sizes), sample_sizes)
sample_sizes <- as.data.frame(sample_sizes)
names(sample_sizes) <- c("Moderator_levels", "Effect_sizes", "Studies")
sample_sizes$Moderator_levels <- str_replace(sample_sizes$Moderator_levels, "^([a-z])", toupper)
n_row <- nrow(sample_sizes)
sample_sizes$Moderator_levels[(n_row - 1):n_row] <- c("Aquatic", "Terrestrial")
kable(sample_sizes, format = "html", row.names = FALSE) %>%
kable_styling("striped", full_width = FALSE, position = "left") %>%
row_spec(0, font_size = 16, bold = TRUE) %>%
pack_rows("Acoustic indices", 1, 11) %>%
pack_rows("Biodiversity metrics", 12, 16) %>%
pack_rows("Diversity source", 17, 18) %>%
pack_rows("Environment", 19, 20)| Moderator_levels | Effect_sizes | Studies |
|---|---|---|
| Acoustic indices | ||
| ACI | 113 | 25 |
| ADI | 38 | 12 |
| AEI | 34 | 8 |
| AR | 18 | 5 |
| BIO | 36 | 10 |
| H | 55 | 16 |
| Hf | 12 | 3 |
| Ht | 15 | 4 |
| M | 5 | 2 |
| NDSI | 33 | 10 |
| NP | 5 | 2 |
| Biodiversity metrics | ||
| Abundance | 27 | 6 |
| Diversity | 49 | 9 |
| Richness | 187 | 21 |
| Sound_abundance | 66 | 11 |
| Sound_richness | 35 | 3 |
| Diversity source | ||
| Acoustic | 200 | 26 |
| No_acoustic | 164 | 11 |
| Environment | ||
| Aquatic | 95 | 10 |
| Terrestrial | 269 | 24 |
We gathered studies from 6 of the 7 continents (no studies in Antarctica). Most studies were conducted in USA, France, Australia and Brazil.
knitr::include_graphics("rmd/mapa.png")Figure S1 - The geographic distribution of the study sites corresponding to the 35 studies used in meta-analysis. The coloring of countries exhibits a white to black gradient relative to an increase in the number of studies contributed by each country. The colored dots discriminate between different groups of studied taxon.
The performance of acoustic indices as a biodiversity indicators was mainly assessed with species richness and sound abundance as biodiversity metrics.
knitr::include_graphics("rmd/Fig.S3a.jpg", )Figure S2 - Number of studies per acoustic index classified by the diversity measure used to assess the correlation between the acoustic index and biodiversity.
Most studies collected in our dataset assessed the relation between acoustic indices and biodiversity using recordings of bird sounds.
knitr::include_graphics("rmd/Fig.S3b.jpg")Figure S3 - Number of studies per acoustic index classified by their studied taxon.
We clustered effect sizes within their corresponding studies and conducted multilevel meta-analysis using Fisher’s Z as our response variable. The multilevel structure accounted for the correlation structures within studies and thus allowed the inclusion of multiple effect sizes per study.
We tested whether acoustic indices were good estimators of biodiversity by computing an intercept-only model. The resulting summary effect size not only gives a clue of whether acoustic indices are performing well in estimating biodiversity across the literature, but also allows us to check if there is substantial heterogeneity in our effect sizes that could be explained by moderators.
Intercept-only meta-analysis outputres_main <- rma.mv(z, var, random = ~1 | id/entry, data = df_tidy)
res_main##
## Multivariate Meta-Analysis Model (k = 364; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0458 0.2139 34 no id
## sigma^2.2 0.1755 0.4190 364 no id/entry
##
## Test for Heterogeneity:
## Q(df = 363) = 2220.9097, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.3461 0.0577 6.0014 <.0001 0.2331 0.4591 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Table S5 - Resulting estimates from intercept-only model converted to Pearson’s correlation. “Estimate” is the Pearson’s r summary effect size. “CI.lb” and “CI.ub” are the confidence intervals lower and upper bounds, respectively.
r_main <- sapply(c(r = res_main$b, CI.lb = res_main$ci.lb, CI.ub = res_main$ci.ub), z2r)
r_main_pander <- r_main
names(r_main_pander) <- c("Estimate", names(r_main)[2:3])
pander(r_main_pander)| Estimate | CI.lb | CI.ub |
|---|---|---|
| 0.3329 | 0.2289 | 0.4294 |
The summary estimate indicates that acoustic indices showed a moderate correlation with biodiversity metrics. However this result does not say nothing about differences in performance between acoustic indices or context-dependencies due to, for example, different environments or diversity metrics. For this we would need to inspect moderators, but before we need to check if our intercept-only model has unexplained variance that can be partitioned by our chosen moderators.
The amount of heterogeneity in effect sizes can be coarsely inspected by plotting all effect sizes and respective variances, and see their dispersion along the x-axis.
df_all_plt <- mutate(df_tidy, z = z2r(z), var = z2r(var))
r_main <- as.data.frame(t(r_main))
nudge <- 2
overall_y <- -nudge - 1
ggplot(data = df_all_plt, aes(x = z, y = reorder(entry, -z))) +
geom_errorbarh(aes(xmin = z - 1.96 * var, xmax = z + 1.96 * var),
height = 0, size = 0.5, color = "grey",
position = position_nudge(y = nudge)) +
geom_point(size = 0.8, color = "darkgreen",
position = position_nudge(y = nudge)) +
geom_segment(aes(x = z2r(res_main$b), y = overall_y,
xend = z2r(res_main$b),
yend = nrow(df_all_plt) + nudge + 10),
color = alpha("forestgreen", 0.7), linetype = 2, size = 0.5) +
geom_vline(xintercept = 0, linetype = 1) +
# Insert overall estimate
geom_errorbarh(aes(xmin = r_main$CI.lb, xmax = r_main$CI.ub, y = overall_y),
color = "grey") +
geom_point(data = r_main, aes(x = r, y = overall_y), size = 3, color = "forestgreen") +
geom_hline(yintercept = nudge - 1, color = alpha("black", 0.5), linetype = 5, size = 1) +
annotate("text", x = -1.4, y = overall_y, label= "Overall estimate", size = 4, adj = "right") +
scale_y_discrete(expand = c(0.025, 0.01)) +
xlab("Effect size (r)") +
ylab("Dataset entries ordered by effect size magnitude") +
theme_minimal() +
theme(axis.text.x = element_text(size = 12, color = "black"),
axis.text.y = element_blank(),
axis.line.x = element_line(color = "black"),
axis.line.y = element_line(color = "black"),
axis.title = element_text(size = 14),
panel.grid.major.y = element_blank(),
legend.position = "none"
)Figure S4 - Pearson correlation effect sizes (r) in ascending order of magnitude from all dataset entries. Effect sizes higher than 0 (vertical line) represent a positive correlation between acoustic indices and diversity. Effect sizes below 0 indicate a negative correlation between acoustic indices and diversity. The green circles above the dashed horizontal line, are effect sizes means and corresponding 95% confidence intervals (grey horizontal lines). Below the dashed line is the summary effect size (green circle) and corresponding 95% confidence interval (grey horizontal lines) obtained after running the intercept-only model.
We quantified heterogeneity with the I² statistic, which estimates the proportion of unknown variation in effect sizes not attributed to sampling error variance.
Table S6 - Unnacounted heterogeneity of the intercept-only model as measured by I2 statistic. Within study heterogeneity (level 2) corresponds to the unnacounted variation that is found on effect sizes within studies, and between study heterogeneity corresponds to the unnacounted variation between studies (level 3).
font_css <- "font-family: Arial"
Is <- multilevel_I(res_main) * 100
Is_df <- data.frame(Is[1], Is[2])
names(Is_df) <- c("Within study", "Between study")
rownames(Is_df) <- c("% Unexplained variation")
total_I <- paste("Total heterogeneity: ", round(Is[1] + Is[2], 2), "%")
header <- c(3)
names(header) <- c(total_I)
kable(Is_df, format = "html", digits = 2) %>%
kable_styling(c("striped", "bordered"), full_width = FALSE,
position = "center") %>%
add_header_above(header, font_size = 16, bold = TRUE,
extra_css = font_css) %>%
row_spec(0, font_size = 14, extra_css = font_css) %>%
row_spec(1, font_size = 12, align = "center")| Within study | Between study | |
|---|---|---|
| % Unexplained variation | 17.61 | 67.52 |
mlm.variance.distribution(res_main)Figure S5 - Visual representation of how variance was distributed over the multilevel structure of the intercept-only model. Within study heterogeneity (level 2) corresponds to the unnacounted variation that is found on effect sizes within studies, and between study heterogeneity corresponds to the unnacounted variation between studies (level 3).
The value of I2 = 85% corresponding to the amount of heterogeneity that remains unnacounted for in the intercept-only model, gives a green signal to proceed with the use of moderators that can potentially explain some of this variation.
We extended the previous intercept-only model with the inclusion of moderators as fixed factors. For these analysis, all moderator levels with less than 5 studies were excluded as low study sizes are more liable to produce biased estimates. This led to the removal of the acoustic indices ‘Hf’, ‘Ht’, ‘M’ and ‘NP’, and the biodiversity parameter ‘sound richness’ from model fitting procedures.
We conducted sub-group analysis with acoustic index as a moderator to assess which acoustic indices best correlate with biodiversity.
To specifically test whether the effect size estimates from each acoustic index were different from zero we removed the model intercept.
df_indices <- clear_moderators(df_tidy, "index")## Levels dropped from dataframe:
## Moderator index
## Hf Ht M NP
res_indices <- rma.mv(z, var, random = ~ 1 | id/entry, mods = ~ index - 1, data = df_indices)
res_indices##
## Multivariate Meta-Analysis Model (k = 327; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0295 0.1716 34 no id
## sigma^2.2 0.1710 0.4135 327 no id/entry
##
## Test for Residual Heterogeneity:
## QE(df = 320) = 1876.0325, p-val < .0001
##
## Test of Moderators (coefficients 1:7):
## QM(df = 7) = 70.5454, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## indexACI 0.3809 0.0685 5.5596 <.0001 0.2466 0.5152 ***
## indexADI 0.2493 0.0977 2.5506 0.0108 0.0577 0.4408 *
## indexAEI 0.0396 0.1048 0.3774 0.7059 -0.1658 0.2449
## indexAR 0.0780 0.1354 0.5756 0.5649 -0.1875 0.3434
## indexBIO 0.1950 0.1012 1.9266 0.0540 -0.0034 0.3934 .
## indexH 0.5511 0.0903 6.1036 <.0001 0.3742 0.7281 ***
## indexNDSI 0.4557 0.1037 4.3944 <.0001 0.2524 0.6589 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Table S7 - Resulting estimates from sub-group analysis. The ‘Estimate’ column is the Pearson correlation effect size; SE is the standard error and CI.lb and CI.up, the lower and upper bounds of the confidence intervals, respectively.
df_pred_indices <- get_predictions(res_indices, intercept = FALSE)
df_pred_indices$coef <- str_remove(df_pred_indices$coef, "index")
names(df_pred_indices) <- c("Index", "Estimate", "SE", "CI.lb", "CI.ub")
pander(df_pred_indices)| Index | Estimate | SE | CI.lb | CI.ub |
|---|---|---|---|---|
| ACI | 0.363 | 0.068 | 0.242 | 0.474 |
| ADI | 0.244 | 0.097 | 0.058 | 0.414 |
| AEI | 0.04 | 0.104 | -0.164 | 0.24 |
| AR | 0.078 | 0.135 | -0.185 | 0.331 |
| BIO | 0.193 | 0.101 | -0.003 | 0.374 |
| H | 0.501 | 0.09 | 0.358 | 0.622 |
| NDSI | 0.427 | 0.103 | 0.247 | 0.578 |
nentries_index <- rowSums(table(df_indices$index, df_indices$id))
nstudies_index <- rowSums(ifelse(table(df_indices$index, df_indices$id) > 0, 1, 0))
n_index <- paste0(nentries_index, " (", nstudies_index, ")")
df_indices_plt <- data.frame("index" = names(nstudies_index),
"es" = z2r(res_indices$beta),
"se" = z2r(res_indices$se),
"ci.lb" = z2r(res_indices$ci.lb),
"ci.ub" = z2r(res_indices$ci.ub),
"n" = nstudies_index)
ggplot(data = df_indices_plt, aes(x = es, y = index)) +
geom_point(aes(color = index), size = 4) +
geom_errorbarh(aes(xmin = ci.lb, xmax = ci.ub, color= index),
height = 0) +
geom_vline(xintercept = 0, linetype = 1) +
geom_vline(xintercept = z2r(res_main$b), color = "forestgreen",
linetype = 2) +
scale_y_discrete(limits = rev(df_indices_plt$index)) +
scale_color_brewer(palette="Dark2") +
theme_minimal() +
xlab("Effect size (r)") +
theme(axis.text.x = element_text(size = 12, color = "black"),
axis.text.y = element_text(size = 13, color = "black"),
axis.line.x = element_line(color = "black"),
axis.line.y = element_line(color = "black"),
axis.title.x = element_text(size = 14),
axis.title.y = element_blank(),
legend.position = "none"
)Figure S6 - Effect size mean estimates (circles) and corresponding 95% confidence intervals (horizontal lines) obtained from sub-group meta-analysis with acoustic indices as the grouping factor. Estimated effect sizes whose 95% confidence intervals do not overlap zero (black vertical line) indicate a positive correlation between acoustic indices and diversity if they are to the right of zero, or a negative correlation if they are to the left of zero. The dashed green vertical line represents the summary effect size obtained from the intercept only meta-analysis.
We used meta-regression to check the effect of multiple moderators on the ability of acoustic indices to estimate biodiversity. We focused on four moderators that could alter the performance of biodiversity estimation, namely:
We set as intercept the following combination of moderator levels: ACI index, species richness, terrestrial environment and non-acoustic data source.
Due to low study sample size between most factor level combinations, we were constrained to use only an additive effects model.
mods <- c("index", "bio", "environ", "diversity_source")
df_full <- clear_moderators(df_tidy, mods)## Levels dropped from dataframe:
## Moderator index
## Hf Ht M NP
## Moderator bio
## sound_richness
## Moderator environ
##
## Moderator diversity_source
##
res_full <- rma.mv(z, var, random = ~1 | id/entry,
mods = ~ index + bio + environ + diversity_source,
data = df_full)
res_full##
## Multivariate Meta-Analysis Model (k = 296; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0355 0.1884 33 no id
## sigma^2.2 0.1738 0.4168 296 no id/entry
##
## Test for Residual Heterogeneity:
## QE(df = 284) = 1730.2577, p-val < .0001
##
## Test of Moderators (coefficients 2:12):
## QM(df = 11) = 27.4277, p-val = 0.0040
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.3590 0.1416 2.5359 0.0112 0.0815 0.6365
## indexADI -0.1294 0.1171 -1.1049 0.2692 -0.3590 0.1001
## indexAEI -0.2916 0.1231 -2.3679 0.0179 -0.5329 -0.0502
## indexAR -0.2735 0.1482 -1.8461 0.0649 -0.5639 0.0169
## indexBIO -0.1449 0.1203 -1.2038 0.2287 -0.3807 0.0910
## indexH 0.1977 0.1092 1.8111 0.0701 -0.0162 0.4117
## indexNDSI 0.0840 0.1260 0.6667 0.5050 -0.1630 0.3310
## bioabundance -0.0815 0.1589 -0.5133 0.6078 -0.3929 0.2298
## biodiversity -0.0420 0.0950 -0.4423 0.6583 -0.2283 0.1442
## biosound_abundance 0.2600 0.1470 1.7690 0.0769 -0.0281 0.5480
## environA -0.0656 0.1484 -0.4422 0.6583 -0.3565 0.2252
## diversity_sourceacoustic -0.0091 0.1464 -0.0623 0.9504 -0.2962 0.2779
##
## intrcpt *
## indexADI
## indexAEI *
## indexAR .
## indexBIO
## indexH .
## indexNDSI
## bioabundance
## biodiversity
## biosound_abundance .
## environA
## diversity_sourceacoustic
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Multicollinearity among our moderators was inspected with VIF and found not to be an issue in our model (VIF < 1.7 for all moderators – threshold value of 3 (Zuur, Ieno, & Elphick, 2010).
Table S8 - VIF values obtained for each moderator level.
vif_meta <- as.data.frame(vif.rma(res_full))
vif_meta <- tibble::rownames_to_column(vif_meta, "Moderators")
colnames(vif_meta)[2] <- "VIF"
pander(vif_meta)| Moderators | VIF |
|---|---|
| indexADI | 1.497 |
| indexAEI | 1.45 |
| indexAR | 1.25 |
| indexBIO | 1.478 |
| indexH | 1.472 |
| indexNDSI | 1.438 |
| bioabundance | 1.244 |
| biodiversity | 1.156 |
| biosound_abundance | 1.454 |
| environA | 1.393 |
| diversity_sourceacoustic | 1.623 |
Substantial heterogeneity remained to be explained after fitting the full model. So other factors not tested or an interaction among our moderators could have the ability to extract even more information from the dataset. For the latter, an increase in available studies on the correlation between acoustic indices and biodiversity is of the utmost important.
# Fit overall model with df_full (without levels with less than 5 studies)
res_main_full <- rma.mv(z, var, random = ~1 | id/entry, data = df_full)
sum_I_main_full <- sum(multilevel_I(res_main_full))
sum_I_full <- sum(multilevel_I(res_full))
cat("Heterogenity after fitting the full model\n\t", sum_I_full, "\n\n",
"Difference heterogeneity between intercept only model and full model\n\t",
sum_I_main_full - sum_I_full)## Heterogenity after fitting the full model
## 0.8616913
##
## Difference heterogeneity between intercept only model and full model
## 0.01210371
Table S9 - Meta-regression results. The column “Coefficients” lists the model intercept and the levels of each moderator. The column “Estimate” is the estimated Pearson (r) correlation. “SE” is the standard error of the estimate. “CI” are the [lower] [upper] bounds of the confidence intervals.
df_pred_tbl <- get_predictions(res_full, format_table = TRUE, clean_labels = TRUE)
pander(df_pred_tbl)| Moderators | Coefficients | Estimate | SE | CI |
|---|---|---|---|---|
| Intercept | Intercept | 0.344 | 0.141 | [0.081] [0.563] |
| Index | ADI | 0.226 | 0.147 | [-0.061] [0.478] |
| Index | AEI | 0.067 | 0.152 | [-0.228] [0.351] |
| Index | AR | 0.085 | 0.17 | [-0.246] [0.399] |
| Index | BIO | 0.211 | 0.149 | [-0.08] [0.469] |
| Index | H | 0.506 | 0.14 | [0.273] [0.682] |
| Index | NDSI | 0.416 | 0.148 | [0.15] [0.626] |
| Bio | Abundance | 0.271 | 0.164 | [-0.047] [0.539] |
| Bio | Diversity | 0.307 | 0.144 | [0.033] [0.537] |
| Bio | Sound abundance | 0.55 | 0.211 | [0.197] [0.777] |
| Environment | Aquatic | 0.285 | 0.142 | [0.012] [0.519] |
| Source | Acoustic | 0.336 | 0.108 | [0.136] [0.51] |
df_pred <- get_predictions(res_full)
colnames(df_pred) <- tolower(str_remove(colnames(df_pred), "\\s.*"))
df_pred$moderators <- df_pred_tbl$Moderators
df_pred$coef <- factor(df_pred_tbl$Coefficients,
levels = rev(df_pred_tbl$Coefficients))
plt_colors <- c("#000000", brewer.pal(n = 4, name = "Dark2"))
ggplot(data = df_pred, aes(x = z2r(estimate), y = coef, color = moderators)) +
geom_point(size = 3) +
geom_errorbarh(aes(xmin = ci.lb, xmax = ci.ub), height = 0, size = 1) +
geom_vline(xintercept = 0, linetype = 1) +
scale_color_manual(values = plt_colors, name = "Moderators") +
theme_minimal() +
xlab("Effect size (r)") +
theme(axis.text.x = element_text(size = 13, color = "black"),
axis.text.y = element_text(size = 13, color = "black"),
axis.line.x = element_line(color = "black"),
axis.line.y = element_line(color = "black"),
axis.title.y = element_blank(),
axis.title.x = element_text(size = 14),
panel.grid.major.y = element_blank(),
legend.title = element_text(hjust = 0.5, size = 14),
legend.text = element_text(size = 14)
)Figure S7 - Effect size mean estimates (circles) and corresponding 95% confidence intervals (horizontal lines) obtained from meta-regression analysis with moderators: acoustic indices (Index), biodiversity metrics (Bio), environment (Environment) and acoustic source (Source). Estimated effect sizes whose 95% confidence intervals do not overlap zero (black vertical line) indicate a positive correlation between acoustic indices and diversity if they are to the right of zero, or a negative correlation if they are to the left of zero.
We checked if our choice of moderators explained some of the variation in our effects sizes by computing a Wald-type test on the null hypothesis that moderator levels’ estimates are jointly equal to zero (Viechtbauer et al., 2015).
Table S10 - Wald-type tests for all moderators (first row), and for each moderator separately (remaining rows). “Q” is the Wald statistic. “df” are the degrees of freedom. “p” is the probability that moderator estimates came from a chi-square distribution, where all estimates are equal to zero. So a p-value < 0.05 gives support against the null hypothesis that moderator levels estimates are equal to zero (i.e. they do not explain variation in effect sizes).
imp_mods <- matrix(nrow = length(mods) + 1, ncol = 4,
dimnames = list(NULL, c("Moderator", "Q", "df", "p")))
imp_mods <- as.data.frame(imp_mods)
# Add importance of all moderators
imp_mods[1, 2:ncol(imp_mods)] <- res_full[c("QM", "m", "QMp")]
imp_mods$Moderator[1] <- "All moderators"
for(i in 2:nrow(imp_mods)){
wald_test <- anova(res_full,
btt=grep(mods[i - 1], rownames(res_full$b)))[c("QM", "m","QMp")]
imp_mods[i, 2:ncol(imp_mods)] <- wald_test
}
imp_mods$Moderator[2:nrow(imp_mods)] <- c("Acoustic indices",
"Biodiv. parameters",
"Environment", "Diversity source")
imp_mods <- mutate_if(imp_mods, is.numeric, round, 3)
pander(imp_mods)| Moderator | Q | df | p |
|---|---|---|---|
| All moderators | 27.43 | 11 | 0.004 |
| Acoustic indices | 22.35 | 6 | 0.001 |
| Biodiv. parameters | 3.561 | 3 | 0.313 |
| Environment | 0.196 | 1 | 0.658 |
| Diversity source | 0.004 | 1 | 0.95 |
We found that the acoustic indices explained most of the variation in our full model. Hence, this suggests that acoustic indices are not equally performing when it comes to estimate biodiversity.
To assess pairwise-comparisons between moderator level estimates, we selected the moderator levels with the highest effect size estimates and compared these with effect size estimates for the other levels of the same moderator. For this, we again use a Wald-type test with one degree of freedom, on the null hypothesis that the difference between the two levels is equal to zero. Note that, if a moderator has only two levels, the comparison is directly retrieved from the model output.
We compare the best indices, H and NDSI, with the other indices. We did not use ACI for comparisons, as comparisons can be obtained directly from the full model output.
The pairwise-comparison for H index, suggest that the H index correlates better with biodiversity than the indices ADI, AEI, AR and BIO.
Table S11 - Wald-type tests for the constrasts between acoustic index H with all other acoustic indices. The column “Compared” expresses the comparison, in this cases it is the difference between the estimate H and the estimate of each of the other acoustic indices. The column “Estimate” is the estimate obtained from the difference expressed in the previous column. “SE” is the standard error of the difference, and CI.lb, CI.up the confidence interval lower and upper bound, respectively. “QM” is the Wald statistic. “p” is the probability that the difference between estimates is equal to zero. Thus, a p-value < 0.05 gives support against the null hypothesis of no difference between the estimate of the H index and the estimate of the other index.
# Test differences between H and other indices
H_comp <- compare_moderators(df_full, res_full, "index", "H")
kable(H_comp, format = "html", digits = 3) %>%
kable_styling(c("strip", "condensed"))| Compared | Estimate | SE | CI.lb | CI.up | QM | p |
|---|---|---|---|---|---|---|
| H - ADI | 0.327 | 0.122 | 0.089 | 0.566 | 7.223 | 0.007 |
| H - AEI | 0.489 | 0.127 | 0.241 | 0.738 | 14.901 | 0.000 |
| H - AR | 0.471 | 0.152 | 0.173 | 0.769 | 9.623 | 0.002 |
| H - BIO | 0.343 | 0.124 | 0.099 | 0.586 | 7.591 | 0.006 |
| H - ACI | 0.198 | 0.109 | -0.016 | 0.412 | 3.280 | 0.070 |
| H - NDSI | 0.114 | 0.130 | -0.141 | 0.369 | 0.765 | 0.382 |
The pairwise-comparison for NDSI index, suggest that the NDSI index correlates better with biodiversity than the indices AEI and AR.
Table S12 - Wald-type tests for the constrasts between acoustic index NDSI with all other acoustic indices. The column “Compared” expresses the comparison, in this cases it is the difference between the estimate NDSI and the estimate of each of the other acoustic indices. The column “Estimate” is the estimate obtained from the difference expressed in the previous column. “SE” is the standard error of the difference, and CI.lb, CI.up the confidence interval lower and upper bound, respectively. “QM” is the Wald statistic. “p” is the probability that the difference between estimates is equal to zero. Thus, a p-value < 0.05 gives support against the null hypothesis of no difference between the estimate of the NDSI index and the estimate of the other index.
# Test differences between NDSI and other indices
NDSI_comp <- compare_moderators(df_full, res_full, "index", "NDSI")
kable(NDSI_comp, format = "html", digits = 3) %>%
kable_styling(c("strip", "condensed"))| Compared | Estimate | SE | CI.lb | CI.up | QM | p |
|---|---|---|---|---|---|---|
| NDSI - ADI | 0.213 | 0.133 | -0.047 | 0.474 | 2.586 | 0.108 |
| NDSI - AEI | 0.376 | 0.138 | 0.106 | 0.645 | 7.442 | 0.006 |
| NDSI - AR | 0.358 | 0.161 | 0.041 | 0.674 | 4.914 | 0.027 |
| NDSI - BIO | 0.229 | 0.135 | -0.036 | 0.494 | 2.869 | 0.090 |
| NDSI - H | -0.114 | 0.130 | -0.369 | 0.141 | 0.765 | 0.382 |
| NDSI - ACI | 0.084 | 0.126 | -0.163 | 0.331 | 0.444 | 0.505 |
Since sound abundance measures seemed to be related with a best performance of the acoustic indices ability to correlate with biodiversity, we use sound abundance to compute pairwise comparisons with the other biodiversity metrics.
The pairwise-comparison for sound abundance gave marginal support (at p < 0.05) for the null hypothesis of no difference between sound abundance and other biodiversity metrics.
Table S13 - Wald-type tests for the contrasts between the biodiversity metric sound abundance with all other biodiversity metrics. The column “Compared” expresses the comparison, in this cases it is the difference between the estimate sound abundance and the estimate of each of the other biodiversity metrics. The column “Estimate” is the estimate obtained from the difference expressed in the previous column. “SE” is the standard error of the difference, and CI.lb, CI.up the confidence interval lower and upper bound, respectively. “QM” is the Wald statistic. “p” is the probability that the difference between estimates is equal to zero. Thus, a p-value < 0.05 gives support against the null hypothesis of no difference between the estimate of the sound abundance metric and the estimate of the other metric.
# Test difference between sound_abundance and other bio levels
sound_abund_comp <- compare_moderators(df_full, res_full, "bio", "sound_abundance")
kable(sound_abund_comp, format = "html", digits = 2) %>%
kable_styling(c("strip", "condensed"))| Compared | Estimate | SE | CI.lb | CI.up | QM | p |
|---|---|---|---|---|---|---|
| sound_abundance - abundance | 0.34 | 0.21 | -0.08 | 0.76 | 2.53 | 0.11 |
| sound_abundance - diversity | 0.30 | 0.17 | -0.03 | 0.64 | 3.09 | 0.08 |
| sound_abundance - richness | 0.26 | 0.15 | -0.03 | 0.55 | 3.13 | 0.08 |
We assessed publication bias both, visually with funnel plots and statistically with Egger’s regression.
A funnel plot usually shows the relationship between effect sizes and standard errors. In a symmetric funnel plot, the dispersion of effect sizes should get narrower as standard error decreases.
Due to the multilevel structure of our dataset, we used meta-analytic residuals instead of effect sizes to reduce the effect of independence assumptions. We should consider publication bias as an issue if residuals are outside the expected symmetry of the funnel shape, and if some of the funnel sections do not contain any residual.
To statistically test for funnel plot symmetry we used Egger’s regression with no intercept. A non-significant inverse variance weighted regression of the residuals over the standard error, indicates that the deviation of the residuals from the funnel plot shape is not greater than what would be expected by chance in a symmetric funnel plots.
# Testing model residuals
resid <- rstandard(res_full)
eggers <- regtest(x = resid$resid, sei =sqrt(df_full$var), model = "lm")funnel(res_full,
back = "white",
xlab = "Model Residuals",
ylab = "Std. Error",
pch = 21,
col = "darkblue",
cex = 1.1,
lwd = 2
)
# Put eggers regression results on funnel plot
eggers_round <- round(eggers$pval, 2)
plt_text <- paste0("Regression test for plot symmetry \n\t\t p = ", eggers_round, "\n\n")
legend(legend = plt_text, x = 0.5, y = -0.01, bg = alpha("darkgrey", 0.2))Figure S7 - Funnel plot (dashed triangle) with the relation between model residuals from the meta-regression model, and effect size standard error. Absence of publication bias is represented by a scattered and symmetric distribution of values (blue hollow dots) within the funnel. The box on the top right is the p-value from Egger’s regression, which means that we failed to reject the null hypothesis of funnel symmetry (p = 0.53).
eggers$fit##
## Call:
## lm(formula = yi ~ X - 1, weights = 1/vi)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -14.9740 -1.0312 -0.2052 0.8569 17.3423
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## X -0.12346 0.04217 -2.927 0.00369 **
## Xsei 0.15381 0.23167 0.664 0.50727
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.466 on 294 degrees of freedom
## Multiple R-squared: 0.05028, Adjusted R-squared: 0.04382
## F-statistic: 7.782 on 2 and 294 DF, p-value: 0.0005091
We could not find strong indications of publication bias. Notwithstanding the visual inspection of the funnel plot shows that there are some gaps in the dispersion of the dots in the funnel plot (special at the top, and at the bottom left corner).
Pseudoreplicated designs were widespread in our selected studies. Therefore, to determine the influence of effect size estimates from pseudoreplicated studies in our meta-analysis, we contrasted the effect size estimate for pseudoreplicated and non-pseudoreplicated studies. For this we conducted a meta-analysis with pseudoreplication as the single binary moderator and observed if the resulting estimate of the contrast between both designs overlapped zero.
Meta-analysis with pseudoreplication moderatorno_out <- capture.output({
df_pseudorep <- clear_moderators(df_tidy, "pseudoreplication")
})
res_pseudorep <- rma.mv(z, var, random = ~1 | id/entry, mods = ~ pseudoreplication,
data = df_pseudorep)
res_pseudorep##
## Multivariate Meta-Analysis Model (k = 364; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0508 0.2254 34 no id
## sigma^2.2 0.1754 0.4188 364 no id/entry
##
## Test for Residual Heterogeneity:
## QE(df = 362) = 2176.1806, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4077, p-val = 0.5231
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.3759 0.0713 5.2759 <.0001 0.2363 0.5156 ***
## pseudoreplicationYES -0.0787 0.1232 -0.6385 0.5231 -0.3201 0.1628
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ggplot(data = df_pseudorep, aes(x = pseudoreplication, y = z2r(z))) +
geom_boxplot(aes(color = pseudoreplication)) +
geom_jitter(aes(color = pseudoreplication, size = n), width = 0.2, alpha = 0.5) +
geom_hline(yintercept = z2r(res_main$b), color = "chartreuse4", linetype = 2) +
ylab("Effect Size (r)") +
xlab("Pseudoreplicated") +
scale_color_brewer(palette = "Set1") +
scale_x_discrete(labels = c("No", "Yes")) +
coord_flip() +
theme_minimal() +
theme(legend.position = "none",
axis.text.y = element_text(size = 12, color = "black"),
axis.text.x = element_text(size = 11, color = "black"),
axis.title.y = element_text(size = 14),
axis.title.x = element_text(size = 12),
axis.line.x = element_line(color = "black")
)Figure S8 - Boxplot comparing effect size mean values of sampling designs considered pseudoreplicated (“Yes” on the vertical axis) against sampling designs not considered pseudoreplicated (“No” on the vertical axis). The circles represent each individual effect size mean value, and the circle size indicates the relative sample size of the effect size. The dashed green vertical line shows the position of summary effect size obtained from the intercept only meta-analysis.
We failed to find differences between the estimates of pseudoreplicated and non-pseudoreplicated designs. Thus, we believe that our adjustment of sample sizes, was a sufficient treatment for pseudoreplication in our meta-analysis.
We visually inspected the presence of outlier studies using Cook’s distance clustered by studies. The Cook’s distance for a given study, refers to how far, on average, effect size estimates will move if the study in question is dropped from model fitting (Viechtbauer & Cheung, 2010). We considered a study an outlier if its Cook’s distance was higher than the average of all computed Cook’s distances.
### Cook's distances for each study!
cooks_dist <- cooks.distance(res_full, cluster=df_full$id)
df_full$id <- as.character(df_full$id)
df_cooks <- data.frame(id = names(cooks_dist), cooks_dist = cooks_dist)
df_cooks <- df_cooks %>%
left_join(df_full, by = "id") %>%
select(id, authors, cooks_dist)
# Remove leading spaces
df_cooks$authors <- str_remove(df_cooks$authors, "^\\s")
df_cooks <- df_cooks %>%
filter(!duplicated(authors)) %>%
arrange(authors)
ggplot(data = df_cooks, aes(x = authors, y = cooks_dist, group = 1)) +
geom_point(color = "deepskyblue4") +
geom_line(color = "deepskyblue4") +
geom_segment(aes(xend=authors), yend = 0, color = "darkgrey", linetype = 2) +
geom_hline(yintercept = mean(cooks_dist), color = "darkred", linetype = 2) +
xlab("Studies") + ylab("Cook's Distance") +
scale_x_discrete(limits = rev(df_cooks$authors)) +
theme_minimal() +
theme(
axis.text = element_text(color = "black"),
axis.line = element_line(color = "black"),
) +
coord_flip() Figure S9 - Cook’s distance values for each study (blue dots on the figure) and average Cook’s distance over all studies indicated as a dashed vertical red line. The Cook’s distance for a given study can be interpreted as the distance between the entire set of predicted values once with this study included and once with the this study excluded from the model fitting procedure. On the y-axis are the studies identified by first author and year. The x-axis corresponds to the Cook’s distance values.
Here, we examine outlier studies to discriminate possible reasons for their influence.
outliers <- df_cooks[which(df_cooks$cooks_dist > mean(df_cooks$cooks_dist)), ]$id
df_outliers <- df_full[which(df_full$id %in% outliers), ]
ggplot(data = df_outliers, aes(x = z2r(z), y = id)) +
geom_boxplot(aes(color = id), fill = NA, width = 0.3) +
geom_jitter(height = 0.1, aes(color = id)) +
scale_color_brewer(palette = "Set2") +
scale_y_discrete(labels = rev(unique(df_outliers$authors))) +
xlab("Effect size (r)") +
geom_vline(xintercept = z2r(res_main$b), color = "chartreuse4", linetype = 2) +
theme_minimal() +
theme(
axis.title.y = element_blank(),
axis.text.y = element_text(size = 11, color = "black"),
axis.text.x = element_text(size = 11, color = "black"),
axis.line.x = element_line(color = "black"),
legend.position = "none"
)Figure S10 - Boxplot and distribution of of effect size values (dots) of the two studies identified as outliers. The y-axis identifies the study, and the x-axis corresponds to the Pearson r effect size. The green vertical dashed line is the summary effect obtained in the intercept-only model.
Both outlier studies used birds as the organism of study, and assessed multiple acoustic indices (Gage et al. (2017) examined ACI, ADI, AEI, BIO, H, NDSI indices; and Mammides et al. (2017) examined ACI, ADI, AEI, AR, BIO, H, NDSI indices). The main difference was that Gage et al. (2017) used acoustic recordings to get biodiversity measures while Mammides et al. (2017) relied on non acoustic sources of biodiversity information.
The box plots and effect size dispersion, suggest that the study by Gage et al. (2017) contributed overdispersed effect sizes values, including some at the lower and high ends of the Pearson effect size scale of (-1 to 1).
The Mammides et al. (2017) study contributed a total 84 effect sizes, also dispersed over a wide range. The number of effect sizes per se (23% of total effect sizes) could be responsible for its high value of Cook’s distance.
We evaluated the robustness of our results by removing the outliers from the dataset, and running the meta-regression model without the outlier studies.
Meta-analysis with outliers removeddf_no_outliers <- df_full[-which(df_full$id %in% outliers), ]
res_no_outliers <- rma.mv(z, var, random = ~1 | id/entry,
mods = ~ index + bio + environ + diversity_source,
data = df_no_outliers)
res_no_outliers##
## Multivariate Meta-Analysis Model (k = 200; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.1225 0.3500 31 no id
## sigma^2.2 0.0557 0.2360 200 no id/entry
##
## Test for Residual Heterogeneity:
## QE(df = 188) = 472.4368, p-val < .0001
##
## Test of Moderators (coefficients 2:12):
## QM(df = 11) = 6.3074, p-val = 0.8521
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.6457 0.1870 3.4522 0.0006 0.2791 1.0123
## indexADI -0.2059 0.1118 -1.8412 0.0656 -0.4251 0.0133
## indexAEI -0.0753 0.1260 -0.5977 0.5500 -0.3224 0.1717
## indexAR -0.0770 0.2082 -0.3700 0.7114 -0.4850 0.3310
## indexBIO -0.0992 0.1192 -0.8319 0.4054 -0.3328 0.1345
## indexH -0.0357 0.1058 -0.3372 0.7360 -0.2430 0.1716
## indexNDSI -0.0164 0.1406 -0.1165 0.9073 -0.2919 0.2591
## bioabundance -0.1566 0.1512 -1.0355 0.3005 -0.4530 0.1398
## biodiversity -0.1064 0.1298 -0.8199 0.4122 -0.3607 0.1479
## biosound_abundance 0.1597 0.2105 0.7587 0.4480 -0.2528 0.5722
## environA -0.1907 0.2057 -0.9271 0.3539 -0.5939 0.2125
## diversity_sourceacoustic -0.1379 0.1909 -0.7226 0.4700 -0.5120 0.2362
##
## intrcpt ***
## indexADI .
## indexAEI
## indexAR
## indexBIO
## indexH
## indexNDSI
## bioabundance
## biodiversity
## biosound_abundance
## environA
## diversity_sourceacoustic
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# With outliers results from full model results
df_pred$df <- "with_outliers"
# No outliers resuls in dataframe
df_no_outliers_plt <- get_predictions(res_no_outliers)
df_no_outliers_plt$moderators <- df_pred$moderators
df_no_outliers_plt$coef <- factor(df_pred$coef,
levels = rev(df_pred$coef))
df_no_outliers_plt$df <- "no_outliers"
df_examine_outliers <- rbind(df_pred, df_no_outliers_plt)
plt_colors <- c("skyblue4", "goldenrod3")#"seagreen")
pd <- position_dodge(0.6)
n_rows <- nrow(res_full$b)
ggplot(data = df_examine_outliers, aes(x = estimate, y = coef, color = df, position = df)) +
geom_point(position = pd, size = 2.3) +
geom_errorbarh(aes(xmin = ci.lb, xmax = ci.ub), height = 0, position = pd, size = 0.8) +
geom_vline(xintercept = 0, linetype = 1, color = "black") +
#scale_y_discrete(labels = rev(y_labels)) +
scale_color_manual(values = plt_colors, name = "Dataset",
labels = c("No outliers", "Full dataset")) +
geom_hline(yintercept= seq(1, n_rows - 1) + 0.5, linetype = 3, color = "black") +
theme_minimal() +
xlab("Effect size (r)") +
theme(axis.text.x = element_text(size = 12, color = "black"),
axis.text.y = element_text(size = 13, color = "black"),
axis.line.x = element_line(color = "black"),
axis.line.y = element_line(color = "black"),
axis.title.y = element_blank(),
axis.title.x = element_text(size = 14),
panel.grid.major.y = element_blank(),
legend.title = element_text(hjust = 0.5, size = 12),
legend.text = element_text(size = 11)
)Figure S11 - Contrast of model estimates obtained with meta-regression analysis over the full dataset (yellow) and over the dataset with outliers removed (blue). Mean estimates are represented with circles and corresponding 95% confidence intervals with horizontal lines. We considered outliers every study that had a Cook’s distance value higher than the mean of all Cook distances. Model moderators were acoustic indices (ADI, AEI, AR, BIO, H, NDSI, with ACI as intercept), diversity metric (Abundance, Diversity, Sound abundance, with Richness as intercept), environment (Aquatic, with Terrestrial as intercept), diversity source (Acoustic, with Non-Acoustic as intercept). The solid vertical black line represents a null effect size.
The results are similar with major overlap between confidence intervals, specially for the most conclusive results in the full dataset. It seems that removing outliers had a tendency to generate stronger mean effect size estimates of the correlation between acoustic indices and biodiversity.
Heterogeneity for the intercept-only model with no outliersres_main_no_outliers <- rma.mv(z, var, random = ~1 | id/entry, data = df_no_outliers)
Is_no_outliers <- multilevel_I(res_main_no_outliers)
sum(Is_no_outliers)## [1] 0.8256989
We visually inspected tendencies in the effect size over year of publication, and impact factor of the journal.
This figure indicates a decrease in effect size values over the years. ?SOME DISCUSSION HERE FOR REASONS? Roughly after the year of 2015, we see a surge in published studies so in the next figure we make a close up of published studies after 2015.
plt_color <- "darkorchid4"
ggplot(df_tidy, aes(x = year, y = z2r(z))) +
geom_jitter(aes(size = n), shape = 21,
fill = alpha(plt_color, 0.5),
color = plt_color) +
geom_hline(yintercept=0, linetype = 2) +
geom_smooth(method='lm', color = plt_color) +
labs(y = "Effect size (r)", x = "Publication Year") +
scale_x_continuous(breaks = seq(min(df_tidy$year),
max(df_tidy$year), by = 2)) +
theme_minimal() +
theme(
axis.line.y = element_line(color = "black"),
axis.text = element_text(color = "black", size = 12),
axis.title = element_text(color = "black", size = 14),
legend.position = "none"
)Figure S12 - Relation between effect size mean values (circles) and publication year. Circle size indicates the relative sample size of each effect size. The fitted line is a simple least squares regression with the corresponding 95% confidence interval region in grey. The dashed horizontal line represents an effect size of 0. Effect size mean values are positioned along the publication year axis with minor random noise to reduce overlapping.
Fitting a regression line over studies from 2015 to 2019, the decreasing tendency persists but it is less pronounced.
df_tidy_after2015 <- df_tidy[df_tidy$year >= 2015, ]
ggplot(df_tidy_after2015, aes(x = year, y = z2r(z))) +
geom_jitter(aes(size = n), shape = 21,
fill = alpha(plt_color, 0.5),
color = plt_color) +
geom_hline(yintercept=0, linetype = 2) +
geom_smooth(method='lm', color = plt_color) +
labs(y = "Effect size (r)", x = "Publication Year") +
scale_x_continuous(limits = c(2015, 2019),
breaks = seq(min(df_tidy_after2015$year),
max(df_tidy_after2015$year),
by = 1)) +
theme_minimal() +
theme(
axis.line.y = element_line(color = "black"),
axis.text = element_text(color = "black", size = 12),
axis.title = element_text(color = "black", size = 14),
legend.position = "none"
)Figure S13 - Relation between effect size mean values (circles) and publication year after 2015 (inclusive) where there is a sudden and prominent rise of publications. Circle size indicates the relative sample size of each effect size. The fitted line is a simple least squares regression with the corresponding 95% confidence interval region in grey. The dashed horizontal line represents an effect size of 0. Effect size mean values are positioned along the publication year axis with minor random noise to reduce overlapping.
Effect size values do not appear to exhibit a tendency when it comes to journal impact factor.
plt_color <- "deeppink4"
ggplot(df_tidy, aes(x = impact_factor, y = z2r(z))) +
geom_jitter(aes(size = n), shape = 21,
fill = alpha(plt_color, 0.5),
color = plt_color,
width = 0.2) +
geom_hline(yintercept=0, linetype = 2) +
geom_smooth(method='lm', color = plt_color) +
labs(y = "Effect size (r)", x = "Impact Factor") +
theme_minimal() +
theme(
axis.line.y = element_line(color = "black"),
axis.text = element_text(color = "black", size = 12),
axis.title = element_text(color = "black", size = 14),
legend.position = "none"
)Figure S14 - Relation between effect size mean values (circles) and journal impact factor. Circle size indicates the relative sample size of each effect size. The fitted line is a simple least squares regression with the corresponding 95% confidence interval region in grey. The dashed horizontal line represents an effect size of 0. Effect size mean values are positioned along the impact factor axis with minor random noise to reduce overlapping.
All code files and supplementary data used in the study can be found in (ADD REPO LINK HERE)
sessionInfo()## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Arch Linux
##
## Matrix products: default
## BLAS: /usr/lib/libopenblasp-r0.3.10.so
## LAPACK: /usr/lib/liblapack.so.3.9.0
##
## locale:
## [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8
## [5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
## [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] RColorBrewer_1.1-2 ggplot2_3.3.1 pander_0.6.3 kableExtra_1.1.0
## [5] MuMIn_1.43.17 compute.es_0.2-5 metafor_2.4-0 Matrix_1.2-18
## [9] stringr_1.4.0 dplyr_1.0.0 png_0.1-7 knitr_1.28
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.1.0 xfun_0.14 purrr_0.3.4 splines_4.0.3
## [5] lattice_0.20-41 colorspace_1.4-1 vctrs_0.3.0 generics_0.0.2
## [9] htmltools_0.4.0 stats4_4.0.3 viridisLite_0.3.0 yaml_2.2.1
## [13] mgcv_1.8-33 rlang_0.4.6 pillar_1.4.4 glue_1.4.1
## [17] withr_2.2.0 jpeg_0.1-8.1 lifecycle_0.2.0 munsell_0.5.0
## [21] gtable_0.3.0 rvest_0.3.5 codetools_0.2-16 evaluate_0.14
## [25] labeling_0.3 highr_0.8 Rcpp_1.0.4.6 readr_1.3.1
## [29] scales_1.1.1 webshot_0.5.2 farver_2.0.3 hms_0.5.3
## [33] digest_0.6.25 stringi_1.4.6 grid_4.0.3 tools_4.0.3
## [37] magrittr_1.5 tibble_3.0.1 crayon_1.3.4 pkgconfig_2.0.3
## [41] ellipsis_0.3.1 xml2_1.3.2 rmarkdown_2.1 httr_1.4.1
## [45] rstudioapi_0.11 R6_2.4.1 nlme_3.1-149 compiler_4.0.3
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